Vehicle analysis environment with displays for vehicle sensor calibration and/or event simulation

ABSTRACT

A vehicle analysis environment includes one or more display screens, such as a display screen wall or an array of display screens. While a vehicle is in the vehicle analysis environment, a vehicle analysis system renders and displays one or more vehicle sensor calibration targets and/or one or more simulated events on the one or more display screens. Vehicle sensors of the vehicle capture sensor data while in the vehicle analysis environment. The sensor data depict the vehicle sensor calibration targets and/or the simulated events that are displayed on the one or more display screens. The vehicle can output actions based on the simulated event and/or can calibrate its vehicle sensors based on the vehicle sensor calibration targets.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of U.S. application Ser. No. 17/195,145, filed on Mar. 8, 2021, entitled VEHICLE ANALYSIS ENVIRONMENT WITH DISPLAYS FOR VEHICLE SENSOR CALIBRATION AND/OR EVENT SIMULATION, which is a continuation of and claims the benefit of U.S. application Ser. No. 17/194,882, filed on Mar. 8, 2021, entitled VEHICLE ANALYSIS ENVIRONMENT WITH DISPLAYS FOR VEHICLE SENSOR CALIBRATION AND/OR EVENT SIMULATION. All of which are expressly incorporated by reference herein in their entireties.

TECHNICAL FIELD

The present technology generally pertains to analysis of sensors that are used by vehicles. More specifically, the present technology pertains to use of one or more displays in a vehicle analysis environment to display calibration targets or simulated events, and to perform vehicle sensor calibration or event simulation using the one or more displays in the vehicle analysis environment.

BACKGROUND

An autonomous vehicle is a motorized vehicle that can navigate without a human driver. An exemplary autonomous vehicle includes a plurality of sensor systems, such as, but not limited to, a camera sensor system, a light detection and ranging (LIDAR) sensor system, or a radio detection and ranging (RADAR) sensor system, amongst others. The autonomous vehicle operates based upon sensor signals output by the sensor systems. Specifically, the sensor signals are provided to an internal computing system in communication with the plurality of sensor systems, wherein a processor executes instructions based upon the sensor signals to control a mechanical system of the autonomous vehicle, such as a vehicle propulsion system, a braking system, or a steering system. Similar sensors may also be mounted onto non-autonomous vehicles, for example onto vehicles whose sensor data is used to generate or update street maps.

A wide range of manufacturing defects or discrepancies can exist in vehicles, sensors, and mounting hardware that affixes the sensors to the vehicles. Because of these discrepancies, different sensors mounted to different vehicles may capture slightly different data, even when those vehicles are at the exact same position, and even when the vehicles are brand new. For example, a lens of one camera may be warped slightly (or include some other imperfection) compared to a lens of another camera, one vehicle may include a newer hardware revision or version of a particular sensor than another, one vehicle's roof may be a few millimeters higher or lower than another vehicle's roof, or a skewed screw used in a mounting structure for a sensor on one vehicle may tilt the mounting structure slightly. Such imperfections and variations in manufacturing can impact sensor readings and mean that there no two vehicles capture sensor readings in quite the same way. Thus, no two vehicles interpret their surroundings via sensor readings in quite the same way. With prolonged use, vehicles can drift even further apart in their sensor readings due to exposure to the elements, for example through exposure to heat, rain, dust, frost, rocks, pollution, vehicular collisions, all of which can further damage, move, warp, or otherwise impact a vehicle or its sensor.

Sensors typically capture data and provide results in a standardized manner that does not, by itself, test or account for intrinsic properties of each sensor, such as the position and angle of the sensor or properties of a lens, or for extrinsic relationships between sensors that capture data from similar areas. Because of this, it can be unclear whether a discrepancy in measurements between two vehicles can be attributed to an actual difference in environment or simply different properties of vehicle sensors. Because autonomous vehicles are trusted with human lives, it is imperative that autonomous vehicles have as robust an understanding of their environments as possible, otherwise a vehicle might perform an action that it should not perform, or fail to perform an action that it should perform, either of which can result in a vehicular accident and put human lives at risk. Other sensor-laden vehicles, such as those that collect data for maps or street-level imagery, can produce unreliable maps or images if they cannot account for the properties of their sensors, which can then in turn confuse both human vehicles and autonomous vehicles that rely on those maps, again risking human life.

There is a need for improved techniques and technologies for calibrating sensors of autonomous vehicles and/or running simulations to test sensors of autonomous vehicles.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-recited and other advantages and features of the present technology will become apparent by reference to specific implementations illustrated in the appended drawings. A person of ordinary skill in the art will understand that these drawings only show some examples of the present technology and would not limit the scope of the present technology to these examples. Furthermore, the skilled artisan will appreciate the principles of the present technology as described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1 illustrates an autonomous vehicle and remote computing system architecture.

FIG. 2A illustrates a camera calibration target with a checkerboard pattern on a planar substrate.

FIG. 2B illustrates a camera calibration target with an ArUco pattern on a planar substrate.

FIG. 2C illustrates a camera calibration target with a crosshair pattern on a planar substrate.

FIG. 2D illustrates a camera calibration target with a dot lattice pattern on a planar substrate.

FIG. 2E illustrates a RADAR sensor calibration target with a trihedral shape.

FIG. 2F illustrates a combined sensor calibration target that uses apertures from a planar substrate surrounded by visual markings to calibrate a camera and a LiDAR sensor.

FIG. 2G illustrates a combined sensor calibration target that is polyhedral and includes markings on multiple surfaces.

FIG. 2H illustrates a display that displays multiple patterns at predetermined poses, each pattern representing a camera calibration target.

FIG. 2J illustrates a display that displays a visual scene corresponding to a simulated event that may be used for calibrating and/or testing one or more cameras of a vehicle.

FIG. 3 illustrates a top-down view of a hallway vehicle analysis environment in which a vehicle traverses a drive path along which the vehicle is flanked by vehicle sensor calibration targets.

FIG. 4A illustrates a perspective view of a dynamic scene vehicle analysis environment in which a turntable is at least partially surrounded by vehicle camera calibration targets rotates a vehicle so that the vehicle can perform intrinsic calibration of its camera sensors.

FIG. 4B illustrates a perspective view of a dynamic scene vehicle analysis environment in which a turntable is at least partially surrounded by combined camera and LIDAR sensor calibration targets and RADAR sensor calibration targets so that the vehicle can perform extrinsic calibration of its camera, LIDAR, and RADAR sensors.

FIG. 5A illustrates a perspective view of a dynamic scene vehicle analysis environment in which a turntable is at least partially surrounded by one or more displays that display virtual camera calibration targets.

FIG. 5B illustrates a perspective view of a dynamic scene vehicle analysis environment in which a platform is at least partially surrounded by one or more displays that display a visual scene corresponding to a simulated event that may be used for calibrating and/or testing one or more cameras of a vehicle.

FIG. 6A illustrates a top-down view of a dynamic scene vehicle analysis environment in which a turntable is at least partially surrounded by various types of vehicle sensor calibration targets.

FIG. 6B illustrates a top-down view of a dynamic scene vehicle analysis environment in which a turntable is at least partially surrounded by display(s) that display virtual camera calibration targets.

FIG. 6C illustrates a top-down view of a dynamic scene vehicle analysis environment in which a stationary platform is at least partially surrounded by display(s) that display virtual camera calibration targets.

FIG. 6D illustrates a top-down view of a dynamic scene vehicle analysis environment in which a stationary platform is at least partially surrounded by display(s) that display a visual scene corresponding to a simulated event that may be used for calibrating and/or testing one or more cameras of a vehicle.

FIG. 7 illustrates a system architecture of a dynamic scene vehicle analysis environment.

FIG. 8 illustrates vehicle operations 800 for rendering and displaying calibration targets and/or simulation scenes for display on one or more displays that are photographed by one or more cameras of a vehicle during a vehicle sensor calibration process and/or a vehicle sensor simulation process.

FIG. 9A illustrates vehicle operations for sensor calibration.

FIG. 9B illustrates vehicle operations for running a vehicle through a simulation.

FIG. 10 is a flow diagram illustrating operation of a calibration environment.

FIG. 11 is a flow diagram illustrating operations for intrinsic calibration of a vehicle sensor using a dynamic scene.

FIG. 12 is a flow diagram illustrating operations for extrinsic calibration of two sensors in relation to each other using a dynamic scene.

FIG. 13 is a flow diagram illustrating operations for interactions between the vehicle and the turntable.

FIG. 14A is a flow diagram illustrating operations for interactions between the vehicle and a lighting system.

FIG. 14B is a flow diagram illustrating operations for interactions between the vehicle and a target control system.

FIG. 15 shows an example of a system for implementing certain aspects of the present technology.

DETAILED DESCRIPTION

Various examples of the present technology are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the present technology. In some instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more aspects. Further, it is to be understood that functionality that is described as being carried out by certain system components may be performed by more or fewer components than shown.

Calibration may be performed using a polyhedral sensor target that includes multiple surfaces. A housing, such as a vehicle, may include a camera and a distance measurement sensor, such as a light detection and ranging (LIDAR) sensor. The housing may move between different positions during calibration, for instance by being rotated atop a turntable. The camera and distance measurement sensor may both capture data during calibration, from which visual and distance measurement representations of the polyhedral sensor target are identified. The camera and distance measurement sensor are calibrated based on their respective representations, for example by mapping vertices within the representations to the same location.

The disclosed technologies address a need in the art for improvements to vehicle sensor calibration technologies. Use of a polyhedral sensor target improves the functioning of sensor calibration by enabling multiple sensors—such as cameras and distance measurement sensors—to be calibrated together using a single sensor calibration target. The three-dimensional nature of the polyhedral sensor target also better ensures that three dimensional objects in the real world will be appropriately recognized when the vehicle is later used in the real world. Calibration using the polyhedral sensor target transform vehicle sensors from an uncalibrated state to a calibrated state, and improve runtime-efficiency, space-efficiency, comprehensiveness of calibration, and consistency of vehicle sensor calibration over prior calibration techniques. The vehicle, its sensors, the vehicle's internal computing device, and the polyhedral sensor target itself are integral to the technology.

FIG. 1 illustrates an autonomous vehicle 102 and remote computing system 150 architecture. The autonomous vehicle 102 can navigate about roadways without a human driver based upon sensor signals output by sensor systems 180 of the autonomous vehicle 102. The autonomous vehicle 102 includes a plurality of sensor systems 180 (a first sensor system 104 through an Nth sensor system 106). The sensor systems 180 are of different types and are arranged about the autonomous vehicle 102. For example, the first sensor system 104 may be a camera sensor system and the Nth sensor system 106 may be a Light Detection and Ranging (LIDAR) sensor system. Other exemplary sensor systems include radio detection and ranging (RADAR) sensor systems, Electromagnetic Detection and Ranging (EmDAR) sensor systems, Sound Navigation and Ranging (SONAR) sensor systems, Sound Detection and Ranging (SODAR) sensor systems, Global Navigation Satellite System (GNSS) receiver systems such as Global Positioning System (GPS) receiver systems, accelerometers, gyroscopes, inertial measurement units (IMU), infrared sensor systems, laser rangefinder systems, ultrasonic sensor systems, infrasonic sensor systems, microphones, or a combination thereof. While four sensors 180 are illustrated coupled to the autonomous vehicle 102, it should be understood that more or fewer sensors may be coupled to the autonomous vehicle 102.

The autonomous vehicle 102 further includes several mechanical systems that are used to effectuate appropriate motion of the autonomous vehicle 102. For instance, the mechanical systems can include but are not limited to, a vehicle propulsion system 130, a braking system 132, and a steering system 134. The vehicle propulsion system 130 may include an electric motor, an internal combustion engine, or both. The braking system 132 can include an engine brake, brake pads, actuators, and/or any other suitable componentry that is configured to assist in decelerating the autonomous vehicle 102. In some cases, the braking system 132 may charge a battery of the vehicle through regenerative braking. The steering system 134 includes suitable componentry that is configured to control the direction of movement of the autonomous vehicle 102 during navigation.

The autonomous vehicle 102 further includes a safety system 136 that can include various lights and signal indicators, parking brake, airbags, etc. The autonomous vehicle 102 further includes a cabin system 138 that can include cabin temperature control systems, in-cabin entertainment systems, etc.

The autonomous vehicle 102 additionally comprises an internal computing system 110 that is in communication with the sensor systems 180 and the systems 130, 132, 134, 136, and 138. The internal computing system includes at least one processor and at least one memory having computer-executable instructions that are executed by the processor. The computer-executable instructions can make up one or more services responsible for controlling the autonomous vehicle 102, communicating with remote computing system 150, receiving inputs from passengers or human co-pilots, logging metrics regarding data collected by sensor systems 180 and human co-pilots, etc.

The internal computing system 110 can include a control service 112 that is configured to control operation of the vehicle propulsion system 130, the braking system 208, the steering system 134, the safety system 136, and the cabin system 138. The control service 112 receives sensor signals from the sensor systems 180 as well communicates with other services of the internal computing system 110 to effectuate operation of the autonomous vehicle 102. In some embodiments, control service 112 may carry out operations in concert one or more other systems of autonomous vehicle 102.

The internal computing system 110 can also include a constraint service 114 to facilitate safe propulsion of the autonomous vehicle 102. The constraint service 116 includes instructions for activating a constraint based on a rule-based restriction upon operation of the autonomous vehicle 102. For example, the constraint may be a restriction upon navigation that is activated in accordance with protocols configured to avoid occupying the same space as other objects, abide by traffic laws, circumvent avoidance areas, etc. In some embodiments, the constraint service can be part of the control service 112.

The internal computing system 110 can also include a communication service 116. The communication service can include both software and hardware elements for transmitting and receiving signals from/to the remote computing system 150. The communication service 116 is configured to transmit information wirelessly over a network, for example, through an antenna array that provides personal cellular (long-term evolution (LTE), 3G, 4G, 5G, etc.) communication.

In some embodiments, one or more services of the internal computing system 110 are configured to send and receive communications to remote computing system 150 for such reasons as reporting data for training and evaluating machine learning algorithms, requesting assistance from remote computing system 150 or a human operator via remote computing system 150, software service updates, ridesharing pickup and drop off instructions etc.

The internal computing system 110 can also include a latency service 118. The latency service 118 can utilize timestamps on communications to and from the remote computing system 150 to determine if a communication has been received from the remote computing system 150 in time to be useful. For example, when a service of the internal computing system 110 requests feedback from remote computing system 150 on a time-sensitive process, the latency service 118 can determine if a response was timely received from remote computing system 150 as information can quickly become too stale to be actionable. When the latency service 118 determines that a response has not been received within a threshold, the latency service 118 can enable other systems of autonomous vehicle 102 or a passenger to make necessary decisions or to provide the needed feedback.

The internal computing system 110 can also include a user interface service 120 that can communicate with cabin system 138 in order to provide information or receive information to a human co-pilot or human passenger. In some embodiments, a human co-pilot or human passenger may be required to evaluate and override a constraint from constraint service 114, or the human co-pilot or human passenger may wish to provide an instruction to the autonomous vehicle 102 regarding destinations, requested routes, or other requested operations.

The internal computing system 110 can, in some cases, include at least one computing system 1500 as illustrated in or discussed with respect to FIG. 15 , or may include at least a subset of the components illustrated in FIG. 15 or discussed with respect to computing system 1500.

As described above, the remote computing system 150 is configured to send/receive a signal from the autonomous vehicle 140 regarding reporting data for training and evaluating machine learning algorithms, requesting assistance from remote computing system 150 or a human operator via the remote computing system 150, software service updates, rideshare pickup and drop off instructions, etc.

The remote computing system 150 includes an analysis service 152 that is configured to receive data from autonomous vehicle 102 and analyze the data to train or evaluate machine learning algorithms for operating the autonomous vehicle 102. The analysis service 152 can also perform analysis pertaining to data associated with one or more errors or constraints reported by autonomous vehicle 102.

The remote computing system 150 can also include a user interface service 154 configured to present metrics, video, pictures, sounds reported from the autonomous vehicle 102 to an operator of remote computing system 150. User interface service 154 can further receive input instructions from an operator that can be sent to the autonomous vehicle 102.

The remote computing system 150 can also include an instruction service 156 for sending instructions regarding the operation of the autonomous vehicle 102. For example, in response to an output of the analysis service 152 or user interface service 154, instructions service 156 can prepare instructions to one or more services of the autonomous vehicle 102 or a co-pilot or passenger of the autonomous vehicle 102.

The remote computing system 150 can also include a rideshare service 158 configured to interact with ridesharing applications 170 operating on (potential) passenger computing devices. The rideshare service 158 can receive requests to be picked up or dropped off from passenger ridesharing app 170 and can dispatch autonomous vehicle 102 for the trip. The rideshare service 158 can also act as an intermediary between the ridesharing app 170 and the autonomous vehicle wherein a passenger might provide instructions to the autonomous vehicle to 102 go around an obstacle, change routes, honk the horn, etc.

The rideshare service 158 as depicted in FIG. 1 illustrates a vehicle 102 as a triangle en route from a start point of a trip to an end point of a trip, both of which are illustrated as circular endpoints of a thick line representing a route traveled by the vehicle. The route may be the path of the vehicle from picking up the passenger to dropping off the passenger (or another passenger in the vehicle), or it may be the path of the vehicle from its current location to picking up another passenger.

The remote computing system 150 can, in some cases, include at least one computing system 1500 as illustrated in or discussed with respect to FIG. 15 , or may include at least a subset of the components illustrated in FIG. 15 or discussed with respect to computing system 1500.

FIG. 2A illustrates a camera calibration target 200A with a checkerboard pattern 210A on a planar substrate 205. The sensor calibration target 200A illustrated in FIG. 2A is a planar board made from a substrate 205, with a pattern 210A printed, stamped, engraved, imprinted, or otherwise marked thereon. The pattern 210A of FIG. 2A is a checkerboard pattern. The substrate 205 may be paper, cardboard, plastic, metal, foam, or some combination thereof. The substrate 205 may in some cases include a translucent or transparent surface upon which the pattern 210A is printed, and which a light source may provide illumination through. The substrate 205 may in some cases include a retroreflective surface upon which the pattern 210A is printed. The retroreflective property of the surface may be inherent to the material of the substrate 205 or may be a separate layer applied to the surface of the substrate, for example by adhering a retroreflective material to the substrate 205 or by painting (e.g., via a brush, roller, or aerosol spray) the substrate 205 with a retroreflective paint. A reflective or retroreflective property may in some cases improve detection using RADAR, LiDAR, or other EmDAR sensors. The material and shape of the substrate 205 may also be selected such that the material and/or shape produces a high amount of acoustic resonance or acoustic response to improve detection using SONAR or SODAR sensors. In some cases, the substrate 205, and therefore the target 200A, may be concave, convex, otherwise curved, or some combination thereof. The substrate 205 may in some cases include devices, such as speakers, heat sources, or light sources, that allow improved detection by microphones, infrared sensors, or cameras, respectively.

The sensor calibration target 200A illustrated in FIG. 2A is useful for calibration of a camera of the vehicle, or other sensor that captures visual data. In particular, a camera with a pattern/image/feature recognition system running on computer system 110 can identify the checkerboard pattern 210A of FIG. 2A, and can identify points representing vertices between the dark (black) and light (white) checkers. By drawing lines connecting these points, the camera and computer system 110 can generate a grid. If the camera has a wide-angle lens, such as a fisheye lens or a barrel lens, the resulting grid will be warped so that some checkers will appear curved rather than straight, and so that checkers near the edges of the camera's point of view will appear more squashed, while checkers near the center of the camera's point of view will appear larger and more even. A rectilinear lens provides a similar, is opposite, effect. Based on prior knowledge of what the checkerboard pattern and resulting grid should look like (e.g., straight edges for each checker), and its original dimensions, compared against what its representation looks like as captured by the camera, the camera and computing system 110 may identify the distortion/warping effect of the lens and counteract this distortion/warping effect by applying an opposite distortion/warping effect. The camera and computing system 110 may also identify other parameters of the camera this way, such as position parameters (x, y, z, roll, pitch, yaw), any lens color to be filtered out, any crack or defect in the lens to be filtered out, or a combination thereof

The sensor calibration target 200A illustrated in FIG. 2A is useful for detection by, and calibration of, a distance measurement sensor of the vehicle, such as a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, at least in that the shape of the planar substrate 205 can be detected by the distance measurement sensor. For example, flat planar vision targets such as the target 200A can be detected by lidar by relying on planar geometry estimates and using the returned intensity. While FIG. 2A illustrates a square or rectangular substrate 205, the substrate 205 may be circular, semicircular, ellipsoidal, triangular, quadrilateral (trapezoid, parallelogram), pentagonal, hexagonal, heptagonal, octagonal, nonagonal, decagonal, otherwise polygonal, or some combination thereof.

In some cases, the sensor calibration target 200A illustrated in FIG. 2A may also function as an infrared sensor target when paired with a heat source such as a heat lamp, an incandescent lamp, or a halogen lamp. In particular, the dark checkers may be darker than the substrate 205 and therefore may absorb more heat than the substrate 205, and may thus be distinguishable from the substrate 205 by an infrared sensor such as an infrared camera.

FIG. 2B illustrates a camera calibration target 200B with an ArUco pattern 210B on a planar substrate 205. The sensor calibration target 200B illustrated in FIG. 2B, like the sensor calibration target 200A illustrated in FIG. 2A, includes a planar board made from a substrate 205, with a pattern 210B printed, stamped, engraved, imprinted, or otherwise marked thereon. The pattern 210B illustrated in FIG. 2B is an ArUco marker pattern, which includes black border and an inner binary matrix/grid (e.g., each square is dark/black or light/white) which determines its identifier.

By detecting the AuUco pattern, the camera and computing system 110 of the vehicle also identify a grid, similarly to the checkerboard, though potentially with fewer points, as some areas of the ArUco pattern may include contiguous dark/black squares or contiguous light/white squares. By identifying the grid from the representation of the ArUco target captured by the camera (e.g. with lens distortion such as parabolic distortion), and comparing it to a known reference image of the ArUco pattern (e.g., without any distortion), any distortions or other differences may be identified, and appropriate corrections may be generated to counteract these distortions or other differences.

The substrate 205 of FIG. 2B may include or be coated with any previously-discussed substrate material and may be warped or shaped in any manner or include any devices discussed with respect to the substrate 205 of FIG. 2A, and therefore may be detected by, and be useful to calibrate a distance measurement sensor of the vehicle, such as a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, and may be detected by a microphone or infrared sensor of the vehicle as well.

In some cases, the sensor calibration target 200B illustrated in FIG. 2B may also function as an infrared sensor target when paired with a heat source such as a heat lamp, an incandescent lamp, or a halogen lamp. In particular, the dark squares/checkers/areas of the ArUco pattern may be darker than the substrate 205 and therefore may absorb more heat than the substrate 205, and may thus be distinguishable from the substrate 205 by an infrared sensor such as an infrared camera.

FIG. 2C illustrates a camera calibration target 200C with a crosshair pattern 210C on a planar substrate 205. The sensor calibration target 200C illustrated in FIG. 2C, like the sensor calibration target 200A illustrated in FIG. 2A, includes a planar board made from a substrate 205, with a pattern 210C printed, stamped, engraved, imprinted, or otherwise marked thereon. The pattern 210C illustrated in FIG. 2C is an crosshair marker pattern, which includes four dark/black lines and two dark/black circles centered on a light/white background, and with a gap in the dark/black lines near but not at the center, effectively leaving a “+” symbol in the very center.

The camera and computing system 110 can identify the target 200C by identifying the circles, the lines, and the intersections of the same. In doing so, the crosshair pattern is identified from the representation of the target 200C captured by the camera (e.g. with lens distortion), and can be compared it to a known reference image of the crosshair pattern target 200C (e.g., without any distortion). As with the checkerboard and ArUco targets, any distortions or other differences may be identified, and appropriate corrections may be generated to counteract these distortions or other differences.

The substrate 205 of FIG. 2C may include or be coated with any previously-discussed substrate material and may be warped or shaped in any manner or include any devices discussed with respect to the substrate 205 of FIG. 2A, and therefore may be detected by, and be useful to calibrate a distance measurement sensor of the vehicle, such as a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, and may be detected by a microphone or infrared sensor of the vehicle as well.

In some cases, the sensor calibration target 200C illustrated in FIG. 2C may also function as an infrared sensor target when paired with a heat source such as a heat lamp, an incandescent lamp, or a halogen lamp. In particular, the dark lines of the pattern 210C may be darker than the substrate 205 and therefore may absorb more heat than the substrate 205, and may thus be distinguishable from the substrate 205 by an infrared sensor such as an infrared camera.

FIG. 2D illustrates a camera calibration target 200D with a dot lattice pattern 210D on a planar substrate 205. The sensor calibration target 200D illustrated in FIG. 2D, like the sensor calibration target 200A illustrated in FIG. 2A, includes a planar board made from a substrate 205, with a pattern 210D printed, stamped, engraved, imprinted, or otherwise marked thereon. The pattern 210D illustrated in FIG. 2D may be referred to as a dot lattice pattern, a dot grid pattern, a dot pattern, a polka dot pattern, or some combination thereof. The pattern 210D includes an arrangement of circular dots. In an alternate embodiment, the dots may be semicircular, oval, square, triangular, rectangular, another polygonal shape, or some combination thereof. The arrangement of circular dots in the pattern 210D is essentially a two-dimensional (2D) “array” or “matrix” or “grid” of dots in which every other row is offset by half a cell, or in which every other column is offset by half a cell. This pattern 200D may also be described as a 2D “array” or “matrix” or “grid” or “lattice” of dots that has been rotated diagonally by approximately 45 degrees. This pattern 210D may also be described as two 2D “array” or “matrix” or “grid” patterns of dots that are offset from one another. Alternate patterns may be used instead, such as patterns with a single “array” or “matrix” or “grid” or “lattice” pattern that is not rotated or offset in any way. In some examples, white or light-colored dots may be included within at least some of the dark dots. In some examples, some dots may be smaller or larger than other dots. In some examples, one or more additional dots may be added between any two other dots in a 2D “array” or “matrix” or “grid” or “lattice” of dots in the pattern.

The camera and computing system 110 can identify the target 200D by identifying the dots in the pattern 200D. In doing so, the vehicle system can identify the dot pattern 210D in the representation of the target 200D captured by the camera (e.g. with lens distortion), and can be compared it to a known reference image of the crosshair pattern target 200D (e.g., without any distortion). As with the checkerboard and ArUco targets, any distortions or other differences may be identified, and appropriate corrections may be generated to counteract these distortions or other differences. For instance, because straight lines should be drawable connecting multiple dots of the pattern 200D, any corrections generated may remove or reduce curvature in such lines from the representation of the target 200D captured by the camera.

The substrate 205 of FIG. 2D may include or be coated with any previously-discussed substrate material and may be warped or shaped in any manner or include any devices discussed with respect to the substrate 205 of FIG. 2A, and therefore may be detected by, and be useful to calibrate a distance measurement sensor of the vehicle, such as a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, and may be detected by a microphone or infrared sensor of the vehicle as well.

In some cases, the sensor calibration target 200D illustrated in FIG. 2D may also function as an infrared sensor target when paired with a heat source such as a heat lamp, an incandescent lamp, or a halogen lamp. In particular, the dark dots of the pattern 210D may be darker than the substrate 205 and therefore may absorb more heat than the substrate 205, and may thus be distinguishable from the substrate 205 by an infrared sensor such as an infrared camera.

While the only patterns 210A-210D discussed with respect to camera sensor targets are checkerboard patterns 210A, ArUco patterns 210B, crosshair patterns 210C, and dot patterns 200D, other patterns that are not depicted herein can additionally or alternatively be used. For example, bar codes, quick response (QR) codes, Aztec codes, Semacodes, Data Matrix codes, PDF417 codes, MaxiCodes, Shotcodes, High Capacity Color Barcodes (HCCB), or combinations thereof may be used in place of or in addition to any of the patterns 210A-210D that can be recognized using the camera and computing device 110.

FIG. 2E illustrates a distance measurement sensor calibration target 220 with a trihedral shape 215. The sensor calibration target 220 of FIG. 2E is made to be detected by, and use for calibration of, a distance measurement sensor, such as a radar sensor (or LIDAR, SONAR, or SODAR) of the vehicle. In particular, the sensor calibration target 220 of FIG. 2E is trihedral in shape, and may be a concave or convex trihedral corner, essentially a triangular corner of a cube. Alternately, it may be a different shape, such as a corner of a different polyhedron (at least portions of all faces of the polyhedron that touch a particular vertex). Such a shape, especially when concave and where perpendicular faces are included, produces a particularly strong radar echo and thus a particularly strong radar cross section (RCS) because incoming radio waves are backscattered by multiple reflection. The RCS of the trihedral corner target is given by: σ=(4·π·a⁴)/(3·λ²), where a is the length of the side edges of the three triangles, and λ is a wavelength of radar transmitter.

The substrate 205 of the sensor calibration target 220 of FIG. 2E may include or be coated with any previously-discussed substrate material and may be warped or shaped in any manner, or include any devices, discussed with respect to the substrate 205 of FIG. 2A. In one embodiment, the substrate 205 of the sensor calibration target 220 of FIG. 2E may be metal, may be electrically conductive, may be reflective or retroreflective, or some combination thereof.

FIG. 2F illustrates a combined distance measurement sensor and camera calibration target 250 with apertures 225 from a planar substrate 205 that are surrounded by visually recognizable markings 230. The combined distance measurement sensor and camera calibration target 250 of FIG. 2F includes multiple apertures 225 in a substrate 205, and includes visual markings or patterns 230 at least partially surrounding each aperture. In particular, the target 250 includes four symmetrical circular or ellipsoid apertures 225 from a light/white substrate 205 with symmetrical dark/black circular or ellipsoid rings 230 around the apertures, with three of the apertures/ring combinations being a first size (e.g., apertures being 30 cm in diameter and the corresponding rings slightly larger) and a fourth aperture/ring combination 240 being a second size (e.g., the aperture being 26 cm in diameter and the corresponding ring slightly larger). The rings around the three larger apertures 225 are likewise larger than the ring around the smaller aperture 240. In some cases, one may be larger than the other three, or two may be larger or smaller than the other two, or some combination thereof. The apertures 225/240 may alternately be referred to as cutouts, holes, voids, orifices, vents, openings, gaps, perforations, interstices, discontinuities or some combination thereof In some cases, different types of surface discontinuities may be used instead of or in addition to the apertures 225/240, such as raised surfaces or bumps that can also be detected by depth/range/distance measurement sensors such as radar or lidar.

The combined distance measurement sensor and camera calibration target 250 of FIG. 2F also includes additional markings or patterns at certain edges of the substrate, identified as target identification (ID) markers 235. The particular combined distance measurement sensor and camera calibration target 250 of FIG. 2F includes target identification (ID) markers 235 on the two sides of the substrate opposite the smaller aperture/ring combination 240, but other combined distance measurement sensor and camera calibration targets 250 may have one, two, three, or four target identification (ID) markers 235 along any of the four sides of the square substrate, or may have target identification (ID) markers 235 in an amount up to the number of sides of the polygonal substrate 205 where the substrate 205 is shaped like a non-quadrilateral polygon. That is, if the substrate 205 is an octagon, each combined combined distance measurement sensor and camera calibration target 250 may have anywhere from zero to eight target identification (ID) markers 235. Different patterns of target identification (ID) markers 235 are further visible in FIG. 5A.

The substrate 205 of FIG. 2F may include or be coated with any previously-discussed substrate material and may be warped or shaped in any manner or include any devices discussed with respect to the substrate 205 of FIG. 2A, and therefore may be detected by, and be useful to calibrate a distance measurement sensor of the vehicle, such as a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, and may be detected by a microphone or infrared sensor of the vehicle as well.

In some cases, the combined distance measurement sensor and camera calibration target 250 may have more or fewer apertures and corresponding visual markings than the four apertures and corresponding visual markings illustrated in FIG. 2F.

In some cases, the sensor calibration target 200F illustrated in FIG. 2F may also function as an infrared sensor target when paired with a heat source such as a heat lamp, an incandescent lamp, or a halogen lamp. In particular, the dark markings 230 around the apertures 225/240 and/or the target ID patterns 235 may be darker than the substrate 205 and therefore may absorb more heat than the substrate 205, and may thus be distinguishable from the substrate 205 by an infrared sensor such as an infrared camera.

FIG. 2G illustrates a combined sensor calibration target 255 that is polyhedral and includes markings (e.g., pattern 210G) on multiple surfaces (e.g., 260A-260C and/or one or more surfaces that are not pictured). The combined sensor calibration target 255 of FIG. 2G is polyhedral and includes multiple surfaces. In particular, the combined sensor calibration target 255 illustrated in FIG. 2G is cubic and this includes six surfaces, three surfaces 260A-260C of which are visible from the perspective in which the combined sensor calibration target 255 is illustrated in FIG. 2G. As illustrated in FIG. 2G, a first surface 260A is illustrated in the lower-right (front-right) portion of the combined sensor calibration target 255, a second surface 260B is illustrated in the lower-left (front-left) portion of the combined sensor calibration target 255, and a third surface 260C is illustrated in the top portion of the combined sensor calibration target 255.

The surfaces 260A-260C of the polyhedral sensor calibration target 255 are marked in a pattern 210G, which is illustrated in FIG. 2G as a checkerboard pattern similar to the checkerboard pattern 210A of FIG. 2A. Each of the three remaining surfaces (not pictured) other than the surfaces 260A-260C of the polyhedral sensor calibration target 255 can also be marked in the pattern 210G and/or in another pattern. The pattern 210G may include any combination of patterns discussed herein; for instance, the pattern 210G may include the checkerboard pattern 210A, the ArUco pattern 210B of FIG. 2B, the crosshair pattern 210C of FIG. 2C, the dot lattice pattern 210D of FIG. 2D, the aperture 225/240 and marking 230 pattern of FIG. 2F, or some combination thereof In some cases, different surfaces may be marked with a different pattern, rather than all of the surfaces being marked with the same pattern.

All three visible surfaces 260A-260C join at a first vertex 270A. The first surface 260A also joins with the second surface 260B and another unseen surface at a second vertex 270B. The first surface 260A also joins with the third surface 260C and another unseen surface at a third vertex 270C. The second surface 260B also joins with the third surface 260C and another unseen surface at a fourth vertex 270D. The first surface 260A also joints with two other unseen surfaces at a fifth vertex 270E. The second surface 260B also joints with two other unseen surfaces at a sixth vertex 270F. The third surface 260C also joints with two other unseen surfaces at a seventh vertex 270G.

Readings from distance measurement sensors, such as light detection and ranging (LIDAR) sensors, may be used to detect surfaces. For instances, readings from a LIDAR sensors may be used to generate a point cloud in which a location of each point relative to the location of the LIDAR sensor is determined based on the distance from the LIDAR sensor measured by the LIDAR sensor at a given point in time as well as the angle and/or orientation of the LIDAR sensor's signal that resulted in that distance measurement. For instance, if the LIDAR sensor sends a signal north and measures a distance of 5 meters when it receives that signal back, then a point is marked in the point cloud as 5 meters north of the location of the LIDAR sensor. If the point cloud includes three or more points that, connected together, form a surface, then the vehicle can infer that a surface exists there. When the polyhedral sensor target 255 is used with the LIDAR sensor with the LIDAR sensor facing the polyhedral sensor target 255 from the perspective illustrated in FIG. 2G, the first surface 260A, the second surface 260B, and the third surface 260C should all be identifiable from the point cloud generated based on the LIDAR sensor's measurements. Based purely on its knowledge of the existence of these three surfaces 260A-260C, the vehicle's internal computer 110 can identify the existence of the first vertex 270A, the edge between the first vertex 270A and the second vertex 270B, the edge between the first vertex 270A and the third vertex 270C, and the edge between the first vertex 270A and the fourth vertex 270D. If the vehicle's internal computer 110 knows that it is pointed at the polyhedral sensor target 255 and knows the shapes and dimensions of its sides, then the vehicle's internal computer 110 can identify the second vertex 270B, third vertex 270C, fourth vertex 270D, fifth vertex 270E, sixth vertex 270F, and seventh vertex 270G.

Visual data (e.g., images, video) captured by one or more cameras may also be used to detect surfaces. In particular, if the internal computing device 110 of the vehicle 102 knows what pattern 210G to expect on the surfaces of the polyhedral sensor target 255, such as the checkerboard pattern illustrated in FIG. 2G, the visual data captured by the camera may be used to identify the angles of the three surfaces 260A-260C relative to one another as well as the shapes of the sides. The visual data captured by the camera may be used to identify the first vertex 270A, the second vertex 270B, the third vertex 270C, the fourth vertex 270D, the fifth vertex 270E, the sixth vertex 270F, the seventh vertex 270G, and all edges in between any two of these vertices 270. In some cases, the visual data captured by the camera may be used to identify the surfaces 260A-260C, vertices 270A-270G, and edges even if no pattern 210G is used, for instance if each of the surfaces 260A-260C of the polyhedral sensor target 255 are painted or dyed different colors, or if shadows and/or reflects on each of the surfaces 260A-260C of the polyhedral sensor target 255 make the surfaces 260A-260C visually discernable, or if the edges and/or vertices of the polyhedral sensor target 255 are painted/marked so as to be visually distinguishable from the surfaces 260A-260C, or some combination thereof.

The polyhedral sensor target 255 may be used for extrinsic calibration between the camera and the LIDAR sensor (or another distance measurement sensor). In particular, one or more of the vertices 270A-270G may be identified both in a visual representation of the polyhedral sensor target 255 that is identified within visual data (e.g., images and/or video) captured by the camera and in a distance measurement representation of the polyhedral sensor target 255 that is identified within distance measurement data (e.g., a point cloud) captured by the distance measurement sensor. The locations of the one or more of the vertices 270A-270G as identified within these representations of the polyhedral sensor target 255 may be mapped to the same location in the real world (e.g., in the calibration environment that the vehicle 102 is in and that the polyhedral sensor target 255 is also in). In some cases, a transformation may be generated to map locations within the distance measurement representation to locations within the visual representation based on the positions of the vertices 270A-270G in these representations.

The polyhedral sensor target 255 may be used for intrinsic calibration of the camera and/or the LIDAR sensor (or another distance measurement sensor). For instance, if the visual representation of the polyhedral sensor target 255 includes warping or distortion of the pattern 210G (e.g., due to lens shape) compared to prior knowledge of what the pattern 210G should look like, the internal computing device 110 of the vehicle 102 may generate a filter that warps or distorts images captured by the camera to correct and/or compensate for the warping or distortion identified. This may in some cases be more useful than a flat target as in the targets 200A-200D of FIGS. 2A-2D and 2F, as multiple surfaces 260A-260C, edges, and corners/vertices 270A-270G are visible and may all be used for calibration.

The surfaces 260A-260C (and other not visible surfaces) of the polyhedral sensor target 255 may be made of a substrate 205, which may include or be coated with any previously-discussed substrate material and may be warped or shaped in any manner or include any devices discussed with respect to the substrate 205 of FIG. 2A.

In some cases, the polyhedral sensor target 255 illustrated in FIG. 2G may also function as an infrared sensor target when paired with a heat source such as a heat lamp, an incandescent lamp, or a halogen lamp. In particular, the dark portions of the pattern 210G may be darker than the substrate 205 and therefore may absorb more heat than the substrate 205, and may thus be distinguishable from the substrate 205 by an infrared sensor such as an infrared camera. The infrared sensor or infrared camera may replace the camera, or may be a third sensor to be calibrated along with a visual camera and distance measurement sensor.

While the polyhedral sensor target 255 is illustrated as cubic in FIG. 2G, other shapes may be used, such as a tetrahedron, a dodecahedron, an icosahedron, an isohedron, a deltahedron, a polycube, a zonohedron, a square pyramid, a rectangular pyramid, a pentagonal pyramid, a pyramid with a base that is a different polygon, a dipyramid, a triangular prism, a rectangular prism shape, a prism whose bases are a different polygon, an antiprism based on any of these polygons, a regular polyhedron, a semi regular polyhedron, an irregular polyhedron, a Platonic solid, an Archimedean solid, a Catalan solid, a Johnson solid, a stellated octahedron, another stellated polyhedron, a truncated polyhedron such as a truncated cube, or some combination thereof In some cases, the polyhedral sensor target 255 may be a polyhedral shape made up of two or more polyhedral shapes that are intersected with one another or coupled to one another at one or more surfaces, such as a dual tetrahedron (also known as a stellated octahedron), a compound of cube and octahedron, a compound of dodecahedron and icosahedron, another dual polyhedral, or some combination thereof In some cases, certain surfaces 260 of the polyhedron may be missing; for instance, rather than seeing the “exterior” of a cube or other polyhedron, the camera and/or distance measurement sensor may see an “interior” of the cube or other polyhedron due to one or more exterior surface(s) being missing that would otherwise block a view of the interior. In such a case, the vertices 270 that are identified may be interior vertices 270 on the interior of the polyhedral sensor target 255 in addition to or instead of exterior vertices 270 on the exterior of the polyhedral sensor target 255. Each of the surfaces 260 of the polyhedron may be flat or curved. Curved surfaces may be, for example, concave or convex or some combination thereof

The polyhedral sensor target 255 of FIG. 2G may in some cases be non-metallic (e.g., made from plastic, cardboard, paper, wood, foam, or some combination thereof) so as not to be as easily detectable by a RADAR sensor, and may include a RADAR target 220 on or adjacent to the polyhedral sensor target 255 at a known location relative to the one or more portions of the polyhedral sensor target 255 (e.g., relative to one or more of the vertices 270A-270G). In this way, the polyhedral sensor target 255 and RADAR target 220 may be used together to extrinsically calibrate the RADAR sensor along with the camera and LIDAR sensor.

FIG. 2H illustrates a display 275 that displays multiple patterns at predetermined poses, each pattern representing a virtual camera calibration target 200H. The display 275 of FIG. 2H may include one or more displays, each of which may be any type of display. For instance, the display 275 can include at least one of a liquid crystal display (LCD), a plasma display, an organic light-emitting diode (OLED) display, a light emitting diode (LED) display, a micro-LED display, a low-temperature poly-silicon (LTPO) display, an electronic ink or “e-paper” display, a projector-based display, a holographic display, any type of display discussed with respect to the output device 1535 of FIG. 15 , another suitable display device, or a combination thereof. In some examples, the display 275 can include a projection surface and a projector that projects content onto the projection surface. In some examples, the display 275 can include, or can be a part of, a display wall. A display wall may be referred to as a display screen wall, a video wall, a projection wall, or a combination thereof.

The display 275, in the example of FIG. 2H, displays three virtual camera targets 200H. In other examples, the display 275 may display less than three virtual camera targets 200H (e.g., 1 or 2 virtual camera targets 200H) or more than three virtual camera targets 200H (e.g., 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more than 20 virtual camera targets 200H). Each virtual camera target 200H includes a pattern that is rendered by a display control system 790 in a predetermined pose. For instance, two of the virtual camera targets 200H in FIG. 2H include the checkerboard pattern 210A, and one of the virtual camera targets 200H in FIG. 2H includes the dot lattice pattern 210D. The patterns include in the virtual camera targets 200H may include any pattern discussed with respect to the patterns 210A, 210B, 210C, 210D, 230, 235, 210G, or a combination thereof. For example, the patterns included in the virtual camera targets 200H may include a checkerboard pattern 210A/210G, an ArUco pattern 210B, a crosshair pattern 210C, a dot lattice pattern 210D, a pattern with circular markings 230, a pattern with target ID markings 235, a bar code pattern, a quick response (QR) code pattern, an Aztec code pattern, a semacode pattern, a data matrix code pattern, a PDF417 code pattern, a MaxiCode pattern, a shotcode pattern, a HCCB pattern, a pattern modeled after a human body, a pattern modeled after a human face, or a combination thereof.

The pose of a virtual camera target 200H can include a position of the virtual camera target 200H and/or an orientation of the virtual camera target 200H. For example, a pose of the virtual camera target 200H can include the horizontal position of the virtual camera target 200H on the display 275, the vertical position of the virtual camera target 200H on the display 275, the simulated depth (e.g., simulated distance in a depth direction orthogonal to the surface of the display 275) of the virtual camera target 200H, the pitch of the virtual camera target 200H, the yaw of the virtual camera target 200H, the roll of the virtual camera target 200H, or a combination thereof. Simulated depth of a virtual camera target 200H can be adjusted by adjusting the size of the virtual camera target 200H as rendered and/or as displayed on the display 275. For instance, if the virtual camera target 200H is rendered and/or displayed in a large size, the virtual camera target 200H may appear to be close to a viewer of the display 275 and thus the simulated depth may be small, indicating that the simulated distance in the depth direction orthogonal to the surface of the display 275 is small. If the virtual camera target 200H is rendered and/or displayed in a small size, the virtual camera target 200H may appear to be far away from a viewer of the display 275 and thus the simulated depth may be large, indicating that the simulated distance in the depth direction orthogonal to the surface of the display 275 is large. The virtual camera target 200H may be, or may include, a two-dimensional surface. The virtual camera target 200H may include a polygonal surface. The virtual camera target 200H may be three-dimensional. For example, the virtual camera target 200H may be a polyhedron, similarly to the target 255 of FIG. 2G. The virtual camera target 200H may be a regular polyhedron, an irregular polyhedron, a rounded 3D shape, a curved 3D shape, or a combination thereof. The virtual camera target 200H may visually mimic any of the targets 200A, 200B, 200C, 200D, 250, 255, or a combination thereof.

In some examples, the virtual camera targets 200H may be warped and/or distorted as displayed on the display 275. For instance, the virtual camera targets 200H may be warped and/or distorted to compensate for curvature of the display, to compensate for an angle of at least a portion of the display relative to a camera of the vehicle whose sensors are being calibrated, to compensate for distortion applied by a camera (e.g., lens distortion) of the vehicle whose sensors are being calibrated, or some combination thereof. The virtual camera targets 200H may be warped and/or distorted as displayed on the display 275 using a perspective distortion and/or a perspective distortion correction.

In some examples, at least one of the virtual camera targets 200H may be stationary as displayed on the display 275. In some examples, at least one of the virtual camera targets 200H may move as displayed on the display 275. Movement of a virtual camera target 200H can include translational movement, such as movement in a horizontal direction along a horizontal axis of the display 275, movement in a vertical direction along a vertical axis of the display 275, movement along a simulated depth direction along a simulated depth axis orthogonal to the surface of the display 275 (e.g., in which movement is simulated by increasing or decreasing the size of the virtual camera target 200H), or a combination thereof. Movement of a virtual camera target 200H can include rotational movement, such as rotation along a yaw axis, rotation along a pitch axis, rotation along a roll axis, or a combination thereof. Rotational movement can include clockwise rotation and/or counter-clockwise rotation along each of one or more axes.

FIG. 2J illustrates a display that displays a visual scene 280 corresponding to a simulated event 285 that may be used for calibrating and/or testing one or more cameras of a vehicle 102. The display 275 may be any type of display 275 discussed with respect to the display 275 of FIG. 2H. The visual scene 280 illustrated in FIG. 2J is a view of a roadway with two vehicles, an intersection with traffic lights (e.g., stoplights), several buildings, several trees, and a walking pedestrian. The simulated event 285 may, for example, include a traffic accident a vehicular accident, a collision, an accident involving a collision with the vehicle undergoing the simulation, an accident involving a collision of other objects, another type of accident, a traffic jam, a hazard on the road, a pedestrian walking into a dangerous area, a dangerous movement by another vehicle, a sharp turn by another vehicle, a sharp acceleration by another vehicle, a sharp deceleration by another vehicle, a lane change by another vehicle, another vehicle coming within a threshold distance of the vehicle undergoing the simulation, a pedestrian coming within a threshold distance of the vehicle undergoing the simulation, another object coming within a threshold distance of the vehicle undergoing the simulation, an event requiring a sharp turn to avoid an accident, an event requiring a sharp acceleration to avoid an accident, an event requiring a sharp deceleration to avoid an accident, an event requiring a lane change to avoid an accident, another event discussed herein, another traffic event that may be useful for testing an autonomous vehicle 102, or a combination thereof. The simulated event 285 may in some cases include sensor data (e.g., camera data such as images and/or video) recorded by sensors 180 (e.g., one or more cameras) a vehicle (e.g., the same vehicle or a different vehicle) over the course of an even time period that occurred before display of the visual scene 280 corresponding to a simulated event 285 on the display 275. For instance, a vehicle 102 may have encountered a difficult driving situation as an event, and the simulated event 285 may simulate this event. The visual scene 280 corresponding to a simulated event 285 may include one or more videos and/or one or more stationary images. Simulations that are at least partially based on past recordings from vehicles may allow

The simulated event 285 may in some cases include data that is computer-rendered and that is at least partially computer-generated (e.g., not from past camera data). The simulated event 285 may allow a vehicle 102 to be tested in situations that would be dangerous to test in the real world to the vehicle 102, to passengers of the vehicle, to other vehicles on the road, to passengers of the other vehicles on the road, to pedestrians, to other property, or some combination thereof. For example, the simulated event 285 may simulate an unexpected accident occurring on the road, and may test the ability of an autonomous vehicle 102 to avoid the accident and/or to minimize damage to the vehicle 102, to passengers of the vehicle, to other vehicles on the road, to passengers of the other vehicles on the road, to pedestrians, to other property, or some combination thereof.

The simulated event 285 may in some cases be a combination of sensor data from a past recording and computer-rendered data that is newly generated (e.g., not from a past recording). For instance, a real event may be altered to make the event more challenging for the autonomous vehicle 102, for instance by adding virtual vehicles, pedestrians, hazards, or some combination thereof.

In some examples, the visual scene 280 corresponding to a simulated event 285, or one or more portions thereof, may be warped and/or distorted as displayed on the display 275. For instance, the visual scene 280 corresponding to a simulated event 285 may be warped and/or distorted to compensate for curvature of the display, to compensate for an angle of at least a portion of the display relative to a camera of the vehicle undergoing the simulation, to compensate for distortion applied by a camera (e.g., lens distortion) of the vehicle undergoing the simulation, or some combination thereof. The visual scene 280 corresponding to a simulated event 285 may be warped and/or distorted as displayed on the display 275 using a perspective distortion and/or a perspective distortion correction.

In some examples, the visual scene 280 corresponding to a simulated event 285, or one or more portions thereof, may be used as virtual camera targets 200H for calibration. For example, a depiction of another vehicle, of a pedestrian, of a stoplight, of a tree, of a building, of a stop sign, of a road hazard, and/or of another element in a visual scene 280 corresponding to a simulated event 285 may be used as a virtual camera target 200H for calibration.

Targets depicted in FIGS. 2A-2J may be modified to be used for calibration of different types of vehicle sensors. Additional targets not depicted in FIGS. 2A-2J may also be used for calibration of different types of vehicle sensors. For example, targets for intrinsic and/or extrinsic calibration of microphones of the vehicle 102 may include speakers, buzzers, chimes, bells, or other audio output devices, optionally in front of, behind, or beside visual markings and/or substrate apertures and/or lighting and/or heating elements. In some cases, certain targets may include substrates that are backlit or frontlit.

FIG. 3 illustrates a top-down view of a hallway vehicle analysis environment 300 in which a vehicle traverses a drive path along which the vehicle is flanked by vehicle sensor calibration targets. The hallway vehicle analysis environment 300 is a type of vehicle analysis environment. A vehicle analysis environment may be used for vehicle sensor calibration. A vehicle analysis environment may be used to display a simulation that is captured by vehicle sensors to test vehicle reactions to a simulated event. A vehicle analysis environment may be referred to as a calibration environment, a sensor calibration environment, a vehicle calibration environment, a vehicle sensor calibration environment, a simulation environment, a sensor simulation environment, a vehicle simulation environment, a vehicle sensor simulation environment, or a combination thereof. The hallway vehicle analysis environment 300 may be referred to as a hallway calibration environment, a hallway sensor calibration environment, a hallway vehicle calibration environment, a hallway vehicle sensor calibration environment, a hallway simulation environment, a hallway sensor simulation environment, a hallway vehicle simulation environment, a hallway vehicle sensor simulation environment, a tunnel calibration environment, a tunnel sensor calibration environment, a tunnel vehicle calibration environment, a tunnel vehicle sensor calibration environment, a tunnel simulation environment, a tunnel sensor simulation environment, a tunnel vehicle simulation environment, a tunnel vehicle sensor simulation environment, a thoroughfare calibration environment, a thoroughfare sensor calibration environment, a thoroughfare vehicle calibration environment, a thoroughfare vehicle sensor calibration environment, a thoroughfare simulation environment, a thoroughfare sensor simulation environment, a thoroughfare vehicle simulation environment, a thoroughfare vehicle sensor simulation environment, a drive path calibration environment, a drive path sensor calibration environment, a drive path vehicle calibration environment, a drive path vehicle sensor calibration environment, a drive path simulation environment, a drive path sensor simulation environment, a drive path vehicle simulation environment, a drive path vehicle sensor simulation environment, or a combination thereof.

The hallway vehicle analysis environment 300 includes a thoroughfare 305 through which a vehicle 102 drives, the thoroughfare 305 flanked on either side by targets detectable by the sensors 180 of the vehicle 102. The thoroughfare 305 may also be referred to as the drive path, the drive channel, the hallway, or the tunnel. One or more of the targets are arranged in a left target channel 310 that is to the left of the vehicle 102 as the vehicle 102 traverses the thoroughfare 305. One or more of the targets are arranged in a right target channel 315 that is to the right of the vehicle 102 as the vehicle 102 traverses the thoroughfare 305. In FIG. 3 , the targets in the right target channel 315 are all checkerboard camera targets 200A as illustrated in FIG. 2A, but they may include any other type of target discussed herein that is used to calibrate any vehicle sensor or combination of vehicle sensors, such as any of the targets of FIGS. 2A-2J. In FIG. 3 , the targets in the left target channel 310 are all virtual camera targets 200H similar to those illustrated in FIG. 2H, and are displayed on one or more display(s) 275. The one or more display(s) 275 in FIG. 3 are arranged as a display wall. The height of the display wall may be, in some examples, at least the height of the vehicle 102 of FIG. 3 . The height of the display wall may be, in some examples, at least a fraction of the height of the vehicle 102 of FIG. 3 . The fraction may be, for example, one fourth, one third, one half, two thirds, three fourths, another fraction between any of the previously listed fractions, or a combination thereof.

The virtual camera targets 200H include 2D planar virtual camera targets 200H having checkerboard patterns 210A (simulating checkerboard camera targets 200A) and dot lattice patterns 210D (simulating dot lattice camera targets 200D), each arranged at various poses. The virtual camera targets 200H include planar virtual camera targets 200H having 3D polyhedral virtual camera targets 200H with checkerboard patterns 210G (simulating the combined camera/LIDAR target 255 of FIG. 2G), each arranged at various poses. The virtual camera targets 200H may simulate any other type of target discussed herein that is used to calibrate any vehicle sensor or combination of vehicle sensors, such as any of the targets of FIGS. 2A-2G. In some examples, the right target channel 315 may include one or more displays 275 displaying one or more virtual camera targets 200H. In some examples, the left target channel 310 may include one or more physical targets (e.g., targets such as those illustrated in FIGS. 2A-2G) instead of or in addition to the virtual camera targets 200H displayed on the one or more displays 275. The left target channel 310 and right target channel 315 may each include a combination of different target types, similarly to the calibration environment of FIGS. 6A-6D; the targets need not all be of a uniform type as illustrated in FIG. 3 .

In some examples, the vehicle 102 drives along the thoroughfare 305, stopping after incremental amounts, for example, every foot, every N feet, every meter, or every N meters, where N is a number greater than zero, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10. At each stop, the vehicle 102 captures data using each of its vehicle sensors, or at least each of the vehicle sensors that it intends to calibrate. The vehicle 102 stopping helps prevent issues caused by sensors running while the vehicle 102 is in motion, such as motion blur or rolling shutter issues in cameras. The vehicle 102 stopping also ensures that sensors can capture data while the vehicle 102 is in the same position, which may be important for extrinsic calibration of two or more sensors with respect to each other so that a location within data gathered by a first vehicle sensor (e.g., a distance measurement sensor such as a LIDAR or radar sensor) can be understood to correspond to a location within data gathered by a second vehicle sensor (e.g., a camera). The vehicle 102 may in some cases traverse the thoroughfare 305 multiple times, for example N times in each direction, where N is, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20.

In some examples, the vehicle 102 may be stationary. In some examples, the sensor calibration targets may move instead of or in addition to the vehicle 102 moving. For instance, the one or more display(s) 275 may change the display positions of the virtual camera targets 200H to move the virtual camera targets 200H. The one or more display(s) 275 may move the display positions of the virtual camera targets 200H to move the virtual camera targets 200H to simulate movement of the vehicle 102 along the thoroughfare 305. Physical targets, such as the camera calibration targets 200A, may be on robotic stands with motor-actuated wheels and/or legs and/or treads that may move. The robotic stands may move along paths to move the physical targets. In some examples, the robotic stands may move to simulate movement of the vehicle 102 along the thoroughfare 305.

The camera calibration targets 200A illustrated in FIG. 3 are illustrated as each mounted on separate easel-style stands. Other types of stands are also possible, such as the stands 410 illustrated in FIGS. 4A-4B, robotic stands with motor-actuated wheels and/or legs and/or treads, or other types of stands. Furthermore, multiple sensor targets, of the same type or of different types may be supported by each stand (as in FIGS. 4A-4B) even though this is not illustrated in FIG. 3 .

The physical sensor targets illustrated in FIG. 3 are illustrated such that some are positioned closer to the thoroughfare 305 while some are positioned farther from the thoroughfare 305. Similarly, the virtual camera targets 200H are illustrated such that some are rendered and/or display larger on the display(s) 275 (simulating a closer position to the thoroughfare 305) while some are rendered and/or display smaller on the display(s) 275 (simulating a farther position from the thoroughfare 305). Additionally, while some physical targets in FIG. 3 are facing a direction perpendicular to the thoroughfare 305, others are angled up or down with respect to the direction perpendicular to the thoroughfare 305. While the physical sensor targets illustrated in FIG. 3 all appear to be at the same height and all appear to not be rotated about an axis extending out from the surface of the target, it should be understood that the sensor targets may be positioned at different heights and may be rotated about an axis extending out from the surface of the target as in the targets of FIGS. 4A and 4B. Similarly, virtual camera targets 200H can be displayed at different heights, tilted up or down, tilted to either side, rotated about one or more axes, or a combination thereof. Together, the distance from the thoroughfare 305, the direction faced relative to the thoroughfare 305, the clustering of targets, the height, and the rotation about an axis extending out from the surface of the target may all be varied and modified, both for physical targets and virtual camera targets 200H, to provide better intrinsic and extrinsic calibration. These variations assist in intrinsic calibration in that collection of data with representations of targets in various positions, rotations, and so forth ensures that targets are recognized as they should be by any sensor, even in unusual positions and rotations, and that any necessary corrections be performed to data captured by sensors after calibration. These variations assist in extrinsic calibration in that the different positions and rotations and so forth ensure that cameras can still detect known patterns despite perspective distortion. These variations assist in extrinsic calibration in that the different positions and rotations and so forth provide more interesting targets for distance measurement sensors, such as lidar, radar, sonar, or sodar, and allow distance measurement sensors to aid in interpretation of optical data collected by a camera of the vehicle 102.

While the thoroughfare 305 of the hallway vehicle analysis environment 300 of FIG. 3 is a straight path, in some cases it may be a curved path, and by extension the left target channel 310 and right target channel 315 may be curved to follow the path of the thoroughfare 305. For example, the display(s) 275 may include one or more curved display(s) and/or flat display(s) arranged in a curve and/or flat display(s) arranged tangential to portions of a curve, to follow the path of the thoroughfare 305.

While the hallway vehicle analysis environment 300 is effective in providing an environment with which to calibrate the sensors 180 of the vehicle 102, and/or simulate an event, the hallway vehicle analysis environment 300 occupies a lot of space. The hallway vehicle analysis environment 300 can require movement of the vehicle, which can introduce inconsistencies in movement and/or inconsistencies in lighting. A hallway vehicle analysis environment 300 can in some cases be set up indoors so that lighting can be better controlled.

FIG. 4A illustrates a perspective view of a dynamic scene vehicle analysis environment 400A in which a turntable 405 is at least partially surrounded by vehicle camera calibration targets 200A rotates a vehicle (not pictured) so that the vehicle can perform intrinsic calibration of its camera sensors. The dynamic scene vehicle analysis environment 400A (as well as other dynamic scene vehicle analysis environments illustrated in FIGS. 4B, 5A-5B, and 6A-6D) is a type of vehicle analysis environment. The dynamic scene vehicle analysis environment 400A (as well as other dynamic scene vehicle analysis environments illustrated in FIGS. 4B, 5A-5B, and 6A-6D) may be referred to as a dynamic scene calibration environment, a dynamic scene sensor calibration environment, a dynamic scene vehicle calibration environment, a dynamic scene vehicle sensor calibration environment, a dynamic scene simulation environment, a dynamic scene sensor simulation environment, a dynamic scene vehicle simulation environment, a dynamic scene vehicle sensor simulation environment, rotational calibration environment, a rotational sensor calibration environment, a rotational vehicle calibration environment, a rotational vehicle sensor calibration environment, a rotational simulation environment, a rotational sensor simulation environment, a rotational vehicle simulation environment, a rotational vehicle sensor simulation environment, a vehicle-surrounding vehicle sensor simulation environment, rotational calibration environment, a rotational sensor calibration environment, a rotational vehicle calibration environment, a rotational vehicle sensor calibration environment, a rotational simulation environment, a rotational sensor simulation environment, a rotational vehicle simulation environment, a rotational vehicle sensor simulation environment, or a combination thereof.

The dynamic scene vehicle analysis environment 400 of FIG. 4A includes a motorized turntable 405 with a platform 420 that rotates about a base 425. In some cases, the platform 420 may be raised above the floor/ground around the turntable 405, with the base 425 gradually inclined up to enable the vehicle 102 to drive up the base 425 and onto the platform 420, or to drive off of the platform 420 via the base 425. A vehicle 102 drives onto the platform 420 of the turntable 405, and the motors actuate to rotate platform 420 of the turntable 405 about the base 425, and to thereby rotate the vehicle 102 relative to the base 425 (and therefore relative to the floor upon which the base 425 rests). While the arrows on the turntable 405 show a counter-clockwise rotation of the platform 420, it should be understood that the platform 420 of the motorized turntable 405 can be actuated to rotate clockwise as well. The turntable 405 is at least partially surrounded by targets mounted on stands 410. In FIG. 4A, the illustrated targets are all checkerboard-patterned camera calibration targets 200A as depicted in FIG. 2A, allowing for calibration of cameras of the vehicle 102. In some examples, such as in FIGS. 4B, 5A, and 6A-6B, the targets around the motorized turntable may include other types of targets discussed herein that can be used to calibrate any vehicle sensor or combination of vehicle sensors.

In one embodiment, the platform 420 of the motorized turntable 405 may be rotated by predetermined intervals (measured in degrees/radians or an amount at a time), for example intervals of ten degrees, in between point the turntable stops so that the vehicle 102 can capture data with its sensors 180. The platform 420 of the motorized turntable 405 can start and stop in this manner, and can eventually perform a full 360 degree rotation in this manner. The motorized turntable 405 may in some cases perform multiple full 360 degree rotations in one or both rotation directions (clockwise and counterclockwise), for example N rotations in each rotation direction, where N is, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20.

In some examples the turntable 405 may be replaced by a stationary platform, as in the platform 570 of FIG. 5B or FIGS. 6C-6D. The platform 570 may represent an area of floor or ground. The platform 570 may represent a platform raised above a floor or ground.

FIG. 4B illustrates a perspective view of a dynamic scene vehicle analysis environment 400B in which a turntable 405 is at least partially surrounded by combined camera and LIDAR sensor calibration targets 250 and RADAR sensor calibration targets 220 so that the vehicle can perform extrinsic calibration of its camera, LIDAR, and RADAR sensors. The dynamic scene vehicle analysis environment 400B of FIG. 4B includes the same motorized turntable 405 as in FIG. 4A. A vehicle 102 drives onto the platform 420 of the turntable 405, and the motors actuate to rotate the platform 420 of the turntable 405, and thereby rotate the platform 420 (and the vehicle 102) about the base 425, clockwise, counter-clockwise, or one then the other. The turntable 405 is at least partially surrounded by targets mounted on stands 410. In FIG. 4B, the illustrated targets include both a set of combined range/camera extrinsic calibration targets 250 as depicted in FIG. 2F and a set of trihedral radar calibration targets 220 as depicted in FIG. 2E. Each stand 410 mounts two combined range/camera extrinsic calibration targets 250 and one trihedral radar calibration targets 220, which in some cases may be a known distance from the combined range/camera extrinsic calibration targets 250 on the stand 410, permitting extrinsic calibration between the radar and the distance measurement sensor (lidar, radar, sonar, sodar) and/or camera calibrated using the combined range/camera extrinsic calibration targets 250.

As the vehicle 102 rotates about the base 425 on the platform 420 of the motorized turntable 405, and/or during stops between rotations, the vehicle 102 and its computer 110 can detect the combined range/camera extrinsic calibration targets 250 using both its distance measurement sensors (e.g., lidar, etc.) and cameras by detecting the apertures 225 with the distance measurement sensors and the markings 230 around the apertures and the target identifier markings 235 with the cameras. In doing so, the vehicle 102 and its computer 110 can detect a center of the circular aperture 225 easily, since distance measurement sensors such as lidar typically provide a point cloud of depth measurements that can help identify where the widest pats of each circle are. The rings 230 detected by the camera will also have the same centers as the apertures, so the distance measurement sensor and camera know they are looking at the exact same locations for each of these center points. Thus, the camera and distance measurement sensor may be extrinsically calibrated so that their positional awareness of the surroundings of the vehicle 102 can be positionally aligned. The extrinsic calibration may, in some cases, output one or more matrices (e.g., one or more transformation matrices) used for transforming a camera location to a distance measurement sensor location or vice versa, via translation, rotation, or other transformations in 3D space. Calibration affects interpretation of data captured by the sensors after calibration is complete. The transformation(s) that are generated during this extrinsic calibration can include one or more types of transformations, including translations, stretching, squeezing, rotations, shearing, reflections, perspective distortion, distortion, orthogonal projection, perspective projection, curvature mapping, surface mapping, inversions, linear transformations, affine transformations, The translational and rotatonal transformations may include modifications to position, angle, roll, pitch, yaw, or combinations thereof In some cases, specific distortions may be performed or undone, for example by removing distortion (e.g., parabolic distortion) caused by use of a specific type of lens in a camera or other sensor, such as a wide-angle lens or a fisheye lens or a macro lens.

The transformation(s) generated by the computer 110 of the vehicle 102 may be used for extrinsic calibration of a first sensor (e.g., the camera) with respect to a second sensor (e.g., LIDAR or RADAR or SONAR or SODAR or another distance measurement sensor), so that the computer 102 can map positions identified in the data output from each sensor to the real world environment around the vehicle 102 (e.g., in the field of view of the sensors 180 of the vehicle 102) and relative to each other, based on known relative positions of features identified within the outputs of each sensor. Such features may include the visual markings of the combined target 250 as identified by the camera, the apertures as identified by the distance measurement sensor, and optionally a trihedral target 220 affixed near or on the target 250 as in the environment 400B of FIG. 4B. For example, if translation of positions in data captured by the first sensor to positions in the real world around the vehicle are already clear through intrinsic calibration, but translation of positions in data captured by the second sensor to positions in the real world around the vehicle are already clear through intrinsic calibration (or vice versa), then the transformation generated through this extrinsic calibration can translate positions in data captured by the second sensor to positions in the real world around the vehicle based on (1) the position in the real world around the vehicle of the data from the first sensor, and (2) the relative positioning of the position in the data from the first sensor and the position in the data from the second sensor. Thus, a sensor that has not been intrinsically calibrated can still be calibrated extrinsically relative to another sensor, and can still benefit from the increase in accuracy granted by the intrinsic calibration of that other sensor.

The trihedral targets 220 can also have a known distance from the combined range/camera extrinsic calibration targets 250, and in some cases specifically from the centers of the apertures 225 and rings 230 of the targets 250, allowing extrinsic calibration between the distance measurement sensor (e.g., radar) that recognizes the trihedral targets 220 and the distance measurement sensor (e.g., lidar) that recognizes the apertures 225 and the camera that recognizes the rings/markings 230.

In other embodiments, the targets around the motorized turntable 405 may include any other type of target discussed herein that is used to calibrate any vehicle sensor or combination of vehicle sensors, such as the target 200A of FIG. 2A, the target 200B of FIG. 2B, the target 200C of FIG. 2C, the target 200D of FIG. 2D, the target 220 of FIG. 2E, the target 250 of FIG. 2F, the target 255 of FIG. 2G, virtual camera target(s) 200H of FIG. 2H, portions of a virtual scene 280 of FIG. 2J that are used as targets, targets with heating elements detectable by infrared sensors of the vehicle 102, targets with speakers detectable by microphones of the vehicle 102, targets with reflective acoustic properties detectable by SONAR/SODAR/ultrasonic/infrasonic sensors of the vehicle 102, or some combination thereof.

The stands 410 used in FIG. 3-6D may include any material discussed with respect to the substrate 205, such as paper, cardboard, plastic, metal, foam, or some combination thereof. In some cases, certain stands may be made of a plastic such polyvinyl chloride (PVC) to avoid detection by certain types of distance measurement sensors, such as radar, which detect metal better than plastic.

In one embodiment, the platform 420 of the motorized turntable 405 may be rotated about the base 425 by predetermined intervals (measured in degrees/radians or an amount at a time), for example intervals of ten degrees, in between point the turntable stops so that the vehicle 102 can capture data with its sensors 180. The intervals may be, for example, 1 degree, 2 degrees, 3 degrees, 4 degrees, 5 degrees, 6 degrees, 7 degrees, 8 degrees, 9 degrees, 10 degrees, 11 degrees, 12 degrees, 13 degrees, 14 degrees, 15 degrees, 16 degrees, 17 degrees, 18 degrees, 19 degrees, 20 degrees, less than 1 degree, more than 20 degrees, or a number of degrees between any two previously listed numbers of degrees. The platform 420 of the motorized turntable 405 can start and stop its rotation via activation and deactivation of its motor(s) 730 in this manner, and can eventually perform a full 360 degree rotation in this manner. The platform 420 of the motorized turntable 405 may in some cases perform multiple full 360 degree rotations about the base 425 in one or both rotation directions (clockwise and counterclockwise). For example, the platform 420 may rotate about the base 420 in one or both directions to complete 1 rotation, 2 rotations, 3 rotations, 4 rotations, 5 rotations, 6 rotations, 7 rotations, 8 rotations, 9 rotations, 10 rotations, 11 rotations, 12 rotations, 13 rotations, 14 rotations, 15 rotations, 16 rotations, 17 rotations, 18 rotations, 19 rotations, 20 rotations, less than 1 rotation, more than 20 rotations, or a number of rotations between any two previously listed numbers of rotations.

FIG. 5A illustrates a perspective view of a dynamic scene vehicle analysis environment 500A in which a turntable 405 is at least partially surrounded by one or more displays 275 that display virtual camera calibration targets 200H. The dynamic scene vehicle analysis environment 500A of FIG. 5A includes the same motorized turntable 405 as in FIGS. 4A-4B, which is at least partially surrounded by virtual camera calibration targets 200H displayed on display(s) 275 that at least partially surround the turntable 405. The display(s) 275 of FIGS. 5A-6D are illustrated as display walls that are curved. In some examples, the displays walls may include flat portions (e.g., flat displays) that are arranged so that each flat display is tangential to a curve, and thus the set of displays in the display wall together approximates a curve. A display wall may include a single large display or a grid, array, matrix, or lattice of smaller displays that function together to extend content from neighboring displays and together display a single scene. In FIG. 5A, the display wall is illustrated as resting on the ground. In some examples, a display wall may be raised, for example on one or more platforms and/or on one or more stands (e.g., stands 410).

The display(s) 275, in the example of FIG. 5A, display five virtual camera targets 200H. Two of the five virtual camera targets 200H of FIG. 5A simulate checkerboard camera calibration patterns 200A of FIG. 2A at different poses. One of the five virtual camera targets 200H of FIG. 5A simulates a combined sensor target 250 of FIG. 2F. One of the five virtual camera targets 200H of FIG. 5A simulates a dot lattice camera calibration pattern 200D of FIG. 2D. One of the five virtual camera targets 200H of FIG. 5A simulates a polyhedral combined sensor target 255 of FIG. 2G.

In some examples, the platform 420 of the motorized turntable 405 may be rotated about the base 425 while the virtual camera targets 200H are display on the display(s) 275. In some examples, at least one of the virtual camera targets 200H may be stationary as displayed on the display(s) 275. In some examples, at least one of the virtual camera targets 200H may move as displayed on the display(s) 275. Movement of a virtual camera target 200H can include translational movement, such as movement in a horizontal direction along a horizontal axis of the display(s) 275, movement in a vertical direction along a vertical axis of the display(s) 275, movement along a simulated depth direction along a simulated depth axis orthogonal to the surface of the display(s) 275 (e.g., in which movement is simulated by increasing or decreasing the size of the virtual camera target 200H), or a combination thereof. Movement of a virtual camera target 200H can include rotational movement, such as rotation along a yaw axis, rotation along a pitch axis, rotation along a roll axis, or a combination thereof. Rotational movement can include clockwise rotation and/or counter-clockwise rotation along each of one or more axes.

In some examples, movement of one or more of the virtual camera targets 200H can simulate rotation of the vehicle 102 and/or of the turntable 405 (from the perspective of the sensors of the vehicle 102) with or without the vehicle 102 and/or turntable 405 actually rotating. The one or more display(s) 275 may move the display positions of the virtual camera targets 200H to move the virtual camera targets 200H to simulate (from the perspective of the sensors of the vehicle 102) rotation of the vehicle 102 as the platform 420 rotates about the base 425. Physical targets may also be part of the dynamic scene vehicle analysis environment 500A. An example of a dynamic scene vehicle analysis environment with both display(s) 275 and physical targets is illustrated in FIG. 6A. Physical targets may be on robotic stands with motor-actuated wheels and/or legs and/or treads that may move. The robotic stands may move along paths to move the physical targets. In some examples, the robotic stands may move to simulate (from the perspective of the sensors of the vehicle 102) rotation of the vehicle 102 as the platform 420 rotates about the base 425.

FIG. 5B illustrates a perspective view of a dynamic scene vehicle analysis environment 500B in which a platform 570 is at least partially surrounded by one or more displays 275 that display a visual scene 280 corresponding to a simulated event 285 that may be used for calibrating and/or testing one or more cameras of a vehicle 102. The dynamic scene vehicle analysis environment 500B includes the same type of display(s) 275 as the dynamic scene vehicle analysis environment 500A, positioned similarly. The turntable 405 of the dynamic scene vehicle analysis environment 500A is replaced with a stationary platform 570 in the dynamic scene vehicle analysis environment 500B. As discussed with respect to FIG. 2J, the visual scene 280 corresponding to the simulated event 285 may be based on previously-recorded camera sensor data, based on computer-generated data (e.g., not from past camera data), or a combination thereof.

The visual scene 280 illustrated in FIG. 5B is the same visual scene 280 illustrated in FIG. 2J, and depicts a roadway with two vehicles, an intersection with traffic lights (e.g., stoplights), several buildings, several trees, and a walking pedestrian. The visual scene 280 corresponding to the simulated event 285 may be displayed on the display(s) 275 as a video in which at least portions of the visual scene 280 move. For example, the roadway in the visual scene 280 can move to simulate driving along the roadway by the vehicle (not pictured) on the platform 570. The other vehicles and/or the pedestrian in the visual scene 280 can move. The traffic lights can change colors, for example between a green light, a yellow light, and a red light. The visual scene 280 may thus be dynamic, and through these movements, the visual scene 280 can simulate the simulated event 285.

FIG. 6A illustrates a top-down view of a dynamic scene vehicle analysis environment 600A in which a turntable 405 is at least partially surrounded by various types of vehicle sensor calibration targets. The dynamic scene vehicle analysis environment 600A of FIG. 6A combines elements of the dynamic scene vehicle analysis environment 400A of FIG. 4A, the dynamic scene vehicle analysis environment 400B of FIG. 4B, and the dynamic scene vehicle analysis environment 500 of FIG. 5A. For instance, the dynamic scene vehicle analysis environment 600A of FIG. 6A includes physical targets, such as camera calibration targets 200A, combined sensor targets 250, and radar calibration targets 220. The dynamic scene vehicle analysis environment 600A of FIG. 6A also includes display(s) 275 that display three virtual camera calibration targets 200H. The dynamic scene vehicle analysis environment 600A of FIG. 6A also includes light sources 620 and/or heat sources 625. Some or all of the heat sources 625 may, in some cases, also be light sources 620, and vice versa. Alternately, independent light sources 620 and heat sources 625 may be used. In some examples, the display(s) 275 may be treated and/or controlled as light sources 620, heat sources 625, or both.

Any of the stands used in FIGS. 3, 4A-4B, and 6A may be any type of stands, such as easel-type stands, tripod-type stands, or the rod stands 410 with wide bases seen in FIGS. 4A-4B. In some cases, the stands 410 may be coupled to mobile robots, which may form the base of the stand 410, and which may be used to move the stand into position from storage and/or to make adjustments as needed and/or back into storage. Any of the stands used in FIGS. 3, 4A-4B, and 6A may include paper, cardboard, plastic, metal, foam, or some combination thereof as previously discussed. Some stands 410 may be non-metallic and intentionally avoid using metal so that the stand does not interfere with the RADAR sensor of the vehicle 102. The stands in some cases may include remote-controllable motors and actuators enabling positions and/or angles of rotation of targets to be manipulated, for example to be more clearly visible given lighting supplied by light sources 620, to receive more heat given heating supplied by heat sources 625, and/or to access a region of a point of view of a particular vehicle sensor from which that vehicle sensor has not captured enough data and therefore around which the vehicle sensor is under-calibrated. In some cases, these motors and their actuators may be controlled wirelessly by the vehicle 102 and/or scene surveying system 610 as discussed further with respect to FIG. 7 and FIG. 14A-B.

The dynamic scene vehicle analysis environment 600A of FIG. 6A also includes a scene surveying system 610, which may include one or more visual cameras, IR cameras (or other IR sensors), one or more distance measurement sensors (e.g., radar, lidar, sonar, sodar, laser rangefinder), any other sensor discussed as being a vehicle sensor, or some combination thereof. This may take the form of a robotic total station (RTS). While the vehicle 102, its sensors 180, and the targets are enough to perform intrinsic calibration of sensors to correct for distortions, for example, and to perform extrinsic calibration of sensors to align locations within data captured by different sensors, for example, in some cases more information may be needed to understand how far these locations within data captured by different sensors are from the vehicle itself. The scene surveying system 610 captures visual and/or range data of at least a subset of the dynamic scene vehicle analysis environment 600, including the motorized turntable 405, at least some of the targets, and the vehicle 102 itself. Key points on the vehicle 102 may be tracked to identify the current pose (rotation orientation/position) of the vehicle 102 (and therefore of the platform 420 about the base 425). Data captured by the scene surveying system 610 can also be sent to the vehicle 102 and used to verify the data captured by the sensors and the intrinsic and extrinsic calibrations performed based on this data. The dynamic scene vehicle analysis environment 600 also includes several light sources 620 set up around the turntable 405. These light sources 620 may include any type of light source, such as incandescent bulbs, halogen bulbs, light emitting diodes (LEDs), and so forth. In some cases, the light sources 620 may include stage lights or spotlights. Mirrors and/or lenses may be used to manipulate light from the light sources 620 to relatively evenly illuminate the targets and/or the dynamic scene vehicle analysis environment 600 as a whole. Additional light sources may be positioned over the turntable 405 or behind some or all targets, especially if targets use a transparent or translucent substrate. The light sources 620 are used to improve readings from cameras of the vehicle and of the scene surveying system 610, and can in some cases be controlled wirelessly by the vehicle 102 and/or scene surveying system 610 as discussed further with respect to FIG. 7 and FIG. 14A-B. The light sources 620 may in some cases also function as heat sources 625 for sensor calibration targets for calibrating IR sensors.

By including targets for intrinsic sensor calibration as well as targets for extrinsic calibration around the turntable 405, the sensors 180 of the vehicle 102 can receive a comprehensive calibration in the dynamic scene vehicle analysis environment 600. The dynamic scene vehicle analysis environments 400A, 400B, 500A, 500B, and 600A-600D of FIGS. 4A-6D have advantages over the hallway vehicle analysis environment 300 of FIG. 3 , such as being more space-efficient, requiring fewer light sources 620 and/or heat sources 625, requiring fewer scene surveying system(s) 610, requiring less power and/or fuel usage by the vehicle 102 (e.g., due to reduced or eliminated need for vehicle-powered vehicle movement), and having increased consistency in target and vehicle positioning (and therefore increased consistency in sensor calibration, simulation testing, and other analyses).

FIG. 6B illustrates a top-down view of a dynamic scene vehicle analysis environment 600B in which a turntable 405 is at least partially surrounded by display(s) 274 that display virtual camera calibration targets 200H. The dynamic scene vehicle analysis environment 600B of FIG. 6B is similar to the dynamic scene vehicle analysis environment 600A of FIG. 6A, but the display(s) 275 stretch almost entirely around the turntable 405 and therefore around the vehicle 102 on the turntable 405. A small gap in the display(s) 275 is left so that the vehicle 102 can enter and exit the dynamic scene vehicle analysis environment 600B through the gap, and so that the scene surveying system 610 can monitor the dynamic scene vehicle analysis environment 600B through the gap. In some examples, however, the gap may be closed so that the display(s) 275 stretch entirely around the turntable 405 and therefore around the vehicle 102 on the turntable 405. The display(s) 275 may be raised so that the vehicle 102 can enter and/or exit the dynamic scene vehicle analysis environment 600B underneath the display(s) 275, and lowered when the vehicle is in the dynamic scene vehicle analysis environment 600B. While the display(s) 275 are illustrated as spanning a ring (or part of a ring) around the turntable 405 and/or the vehicle 102, in some cases the display(s) 275 are illustrated can span a dome (or part of a dome) over the turntable 405 and/or the vehicle 102. If the display(s) 275 span a dome or part of a dome over the turntable 405 and/or the vehicle 102, then the display(s) 275 can also display virtual camera calibration targets 200H above the turntable 405 and/or the vehicle 102. The display(s) 275 are illustrated as displaying ten virtual camera calibration targets 200H in FIG. 6B, but may in some cases display more or fewer virtual camera calibration targets 200H at any given time. The display(s) 275 can display the virtual camera calibration targets 200H in a stationary positon or in motion.

FIG. 6C illustrates a top-down view of a dynamic scene vehicle analysis environment 600C in which a stationary platform 570 is at least partially surrounded by display(s) 275 that display virtual camera calibration targets 200H. The dynamic scene vehicle analysis environment 600C of FIG. 6C is similar to the dynamic scene vehicle analysis environment 600B of FIG. 6B, but the turntable 405 is replaced by a stationary platform 570. Movement of one or more of the virtual camera targets 200H on the display(s) 275 can simulate (from the perspective of the sensors of the vehicle 102) rotation of the vehicle 102 and/or of the platform 570 as if the platform 570 was a motorized turntable 405, all while the platform 570 and/or the vehicle 102 remain stationary and do not rotate. Thus, motion of virtual camera calibration targets 200H on display(s) 275 can achieve some of the same benefits of the motorized turntable 405, without the need for the motorized turntable 405. Technical effects of simulating the effects of the motorized turntable 405 can include more consistency in calibration and/or simulation due to fewer moving parts, since each moving part can introduce some inconsistency (e.g., due to variable friction and/or motor power and/or defects and/or mass of the vehicle 102).

FIG. 6D illustrates a top-down view of a dynamic scene vehicle analysis environment 600D in which a stationary platform 570 is at least partially surrounded by display(s) 275 that display a visual scene 280 corresponding to a simulated event 285 that may be used for calibrating and/or testing one or more cameras of a vehicle 102. The dynamic scene vehicle analysis environment 600D of FIG. 6D is similar to the dynamic scene vehicle analysis environment 600C of FIG. 6C, but the display(s) 275 display the visual scene 280 corresponding to the simulated event 285 instead of displaying virtual camera targets 200H for calibration. The visual scene 280 of FIG. 6D depicts a intersection with traffic lights (e.g., stoplights) ahead of the vehicle 102 on the platform 570; another automobile to the left of the vehicle 102 on the platform 570; and a pedestrian, and a bicyclist riding a bicycle, and a tree to the right of the vehicle 102 on the platform 570. As discussed with respect to FIGS. 2J and 5B, the visual scene 280 corresponding to the simulated event 285 may be based on previously-recorded camera sensor data, based on computer-generated data (e.g., not from past camera data), or a combination thereof. The visual scene 280 corresponding to the simulated event 285 may be displayed on the display(s) 275 as a video in which at least portions of the visual scene 280 move. For example, the tree and traffic lights in the visual scene 280 can move relative to the vehicle 102 to simulate driving along a roadway by the vehicle 102 on the platform 570. The other vehicles and/or the pedestrian and/or the bicyclist in the visual scene 280 can move independently of the vehicle 102's simulated movements along the roadway and/or based on the vehicle 102's simulated movements along the roadway. The traffic lights can change colors, for example between a green light, a yellow light, and a red light. The visual scene 280 may thus be dynamic, and through these movements, the visual scene 280 can simulate the simulated event 285. The simulated event 285 may, for example, include a traffic accident, a vehicular accident, a collision, an accident involving a collision with the vehicle undergoing the simulation, an accident involving a collision of other objects, another type of accident, a traffic jam, a hazard on the road, a pedestrian walking into a dangerous area, a dangerous movement by another vehicle, a sharp turn by another vehicle, a sharp acceleration by another vehicle, a sharp deceleration by another vehicle, a lane change by another vehicle, another vehicle coming within a threshold distance of the vehicle undergoing the simulation, a pedestrian coming within a threshold distance of the vehicle undergoing the simulation, another object coming within a threshold distance of the vehicle undergoing the simulation, an event requiring a sharp turn to avoid an accident, an event requiring a sharp acceleration to avoid an accident, an event requiring a sharp deceleration to avoid an accident, an event requiring a lane change to avoid an accident, another event discussed herein, another traffic event that may be useful for testing an autonomous vehicle 102, or a combination thereof. FIG. 7 illustrates a system architecture 700 of a dynamic scene vehicle analysis environment. The system architecture 700 can be a system architecture for the hallway vehicle analysis environment 300, the dynamic scene vehicle analysis environment 400A, the dynamic scene vehicle analysis environment 400B, the dynamic scene vehicle analysis environment 500A, the dynamic scene vehicle analysis environment 500B, the dynamic scene vehicle analysis environment 600A, the dynamic scene vehicle analysis environment 600B, the dynamic scene vehicle analysis environment 600C, the dynamic scene vehicle analysis environment 600D, or a combination thereof. The system architecture 700 of the dynamic scene vehicle analysis environment of FIG. 7 includes a number of main elements, each with sub-components. The main elements include the autonomous vehicle 102, a dynamic scene control bridge 710, the motorized turntable system 450, a lighting/heating system 760, a target control system 770, a scene surveying system 610, a display control system 790, and a power supply system 702.

The autonomous vehicle 102 includes the one or more sensors 180, the one or more internal computing devices 110, one or more wireless transceivers 705 (integrated with communication service 116), and any other elements illustrated in and discussed with respect to FIG. 1 . The sensors 180 may in some cases include one or more GPRS receivers, Bluetooth® beacon-based positioning receivers, an inertial measurement unit (IMU), one or more cameras, one or more lidar sensors, one or more radar sensors, one or more sonar sensors, one or more sodar sensors, and/or one or more other sensors discussed herein, which may in some cases be used to identify when the vehicle 102 is not on the platform 420, when the vehicle 102 is on the platform 420, when the vehicle 102 in a defined position on the platform (e.g., optionally as guided by guide railings on the platform), when the vehicle 102 have begun rotating from a stopped position, and/or when the platform 420 and/or the vehicle 102 have stopped rotating. The computing device 110 of the vehicle 102 (or its sensors 180) may then automatically communicate one or more signals or messages through wired and/or wireless communication interfaces, for instance through the wireless transceiver 705, to the bridge 710, the computing device 110 of the vehicle 102 (if the communication is straight from the sensors 180), and/or the computing device 745 of the motorized turntable system 405 to convey any of these observations/detections/identifications by the sensors 180, which may be used to trigger various actions, such as rotation or stop of rotation of the turntable 405, collection of sensor data or stop of collection of sensor data at the vehicle 102, or some combination thereof.

The dynamic scene control bridge 710 includes one or more computing devices 715 and one or more wired and/or wireless transceivers 720. The dynamic scene control bridge 710 is optional, but can serve as a “middleman” or “router” between the autonomous vehicle 102 and the remaining main elements of the system architecture 700, such as the dynamic scene control bridge 710, the motorized turntable system 450, the lighting/heating system 760, the target control system 770, the scene surveying system 610, and the power supply system 702. The dynamic scene control bridge 710 can in some cases convert file formats, perform mathematical operations such as operation conversions, or otherwise interpret instructions or data as necessary so that the vehicle 102 can successfully communicate with other elements. In fact, the dynamic scene control bridge 710 can perform similar conversions, mathematical operations, or interpretations in its role as a middleman between any two or more devices of the architecture 700.

The motorized turntable system 405 includes a turntable structure 725 as well as one or more motors, encoders, actuators, and/or gearboxes 730 for actuating rotation of the turntable structure 725 while the vehicle 102 is on it. The motorized turntable 725 may include a platform 420, which is a surface upon which the vehicle 102 rests during calibration. The platform 420 is rotatable about a base 425 of the motorized turntable structure 725, with one or more motors 730 that, when actuated or activated, rotate the platform 420 about the base 425, and which stop the rotation of the platform 420 about the base 425 when the motors 730 are deactivated. For example, the one or more motors 730 may rotate the platform 420 about the base 425 from a first rotational orientation to a second rotational orientation, or alternately back to the first rotational orientation (e.g., if the rotation is a 360 degree rotation or a multiple thereof). A rotational orientation of the platform 420 relative to the base 425, or of the vehicle 102 relative to the base 425, may alternately be referred to as a rotational position. The motorized turntable system 405 may include one or more sensors 735, such as pressure sensors, for example to identify whether or not the vehicle 102 is on the turntable structure 725, whether or not the vehicle 102 is positioned correctly on the turntable structure 725, or how the vehicle's weight is distributed generally or across the platform 420's top surface (which is in contact with the wheels and/or tires vehicle 102) of the turntable structure 725. The sensors 735 may in some cases identify when the platform 420 has no vehicle 102 on it, when the vehicle 102 is on the platform 420, when the vehicle 102 in a defined position on the platform (e.g., optionally as guided by guide railings on the platform), when the platform 420 and/or the vehicle 102 have begun rotating from a stopped position, and/or when the platform 420 and/or the vehicle 102 have stopped rotating. The motorized turntable system 405 may then automatically communicate one or more signals or messages through wired and/or wireless communication interfaces to the bridge 710, vehicle 102, and/or the computing device 745 of the motorized turntable system 405 to convey any of these observations/detections/identifications by the sensors 735, which may be used to trigger various actions, such as rotation or stop of rotation of the turntable 405, collection of sensor data or stop of collection of sensor data at the vehicle 102, or some combination thereof. The controller 740 may be used to control the actuation of the motors, encoders, actuators, and/or gearbox(es) 730, for example to control a speed or rate or angular velocity of rotation, an angular acceleration (or deceleration) of rotation, a direction of rotation (e.g., clockwise or counterclockwise), or some combination thereof. The motorized turntable system 405 includes one or more computing devices 745 and one or more wired and/or wireless transceivers 750, through which it may interact with the vehicle 102, the dynamic scene control bridge 710, or any other element in the architecture 700.

The lighting/heating system 760 includes one or more light sources 620 and/or one or more heat sources 625. The lighting/heating system 760 includes one or more motors and/or actuators 762 for activating or turning on each of the light sources 620 and/or heat sources 625, disabling or turning off each of the light sources 620 and/or heat sources 625, fading or dimming each of the light sources 620 and/or heat sources 625, brightening each of the light sources 620 and/or heat sources 625, increasing or reducing heat output of each of the light sources 620 and/or heat sources 625, moving each of the light sources 620 with an actuated motor (e.g., to output light and/or heat toward a particular target), or some combination thereof. The lighting/heating system 760 includes one or more computing devices 764 and one or more wired and/or wireless transceivers 766, through which it may interact with the vehicle 102, the dynamic scene control bridge 710, or any other element in the architecture 700.

The target control system 770 includes one or more targets and target support structure 772. The targets may include one or more of any of the targets 200A, 200B, 200C, 200D, 220, 250, and/or 255, illustrated in FIG. 2A-2J, any other target described herein, any other sensor calibration target, or some combination thereof. The target support structures may include easel-type support structures, tripod-type support structures, or the rod-type support structures 410 with wide bases seen in FIGS. 4A-4B. The target support structures may include any material discussed with respect to the substrate 205, such as paper, cardboard, plastic, metal, foam, or some combination thereof In some cases, certain stands may be made of a plastic such polyvinyl chloride (PVC) to avoid detection by certain types of distance measurement sensors, such as radar, which detect metal better than plastic.

The targets and/or support structures 720 may in some cases be motorized, and as such, the target control system 770 may include motors and actuators 774 that it can use to move the targets, for example as requested by the vehicle 102 to optimize calibration. For example, the target support structures may include a robotic arm with ball joints and/or hinge joints that may be actuated using the motors and actuators 774 to translate a target in 3D space and/or to rotate a target about any axis. The motors and actuators 773 may alternately only control a single type of movement for a particular target, for example by enabling a target to rotate about the rod of a stand 410. The target support structure 772 may also include wheels or legs, which may be actuated by the motors 774 to enable the entire target support structure 772 to move, and with it, the target(s) it supports. The target control system 770 includes one or more computing devices 776 and one or more wired and/or wireless transceivers 778, through which it may interact with the vehicle 102, the dynamic scene control bridge 710, or any other element in the architecture 700.

The scene surveying system 610 includes a surveying device support structure 780, such as a tripod or any other structure discussed with respect to the target support structure 772, and one or more sensors 782 coupled to the support structure 780. The sensors 782 of the scene surveying system 610, like the sensors 180 of the vehicle 102, may include one or more cameras of any type (e.g., wide-angle lens, fisheye lens), one or more distance measurement sensors (e.g., radar, lidar, emdar, laser rangefinder, sonar, sodar), one or more infrared sensors, one or more microphones, or some combination thereof. Using these, the scene surveying system 610 can capture a representation of the entire dynamic scene, including the vehicle 102, allowing determination of distances between the vehicle 102 and various targets. In some cases, either the vehicle 102 or the scene surveying system 610 or both may request adjustment of lighting and/or heating through the lighting/heating system 760 and/or adjustment of target positioning via the target control system 770. The scene surveying system 610 includes one or more computing devices 784 and one or more wired and/or wireless transceivers 786, through which it may interact with the vehicle 102, the dynamic scene control bridge 710, or any other element in the architecture 700. In some cases, feature tracking and/or image recognition techniques applied using the computing device 784 may be used with the a camera and/or the radar, lidar, sonar, sodar, laser rangefinder, and/or other sensors 782 of the scene surveying system 610 to identify when the platform 420 has no vehicle 102 on it, when the vehicle 102 is on the platform 420, when the vehicle 102 in a defined position on the platform (e.g., optionally as guided by guide railings on the platform), when the platform 420 and/or the vehicle 102 have begun rotating from a stopped position, and/or when the platform 420 and/or the vehicle 102 have stopped rotating. The scene surveying system 610 may then automatically communicate one or more signals or messages through wired and/or wireless communication interfaces to the bridge 710, vehicle 102, and/or motorized turntable system 405 to convey any of these observations/detections/identifications by the scene surveying system 610, which may be used to trigger various actions, such as rotation or stop of rotation of the turntable 405, collection of sensor data or stop of collection of sensor data at the vehicle 102, or some combination thereof. In some cases, the scene surveying system 610 may simply be referred to as a camera or as another sensor that the scene surveying system 610 includes.

The display control system 790 includes one or more display(s) 788. The one or more display(s) 275 of FIGS. 2H, 2J, 5A, 5B, 6A, 6B, 6C, and 6D may be examples of the one or more display(s) 788. Through the one or more displays 788, the display control system 790 may each display at least a portion of one or more virtual camera targets 200H that may be used for calibrating and/or testing one or more cameras of a vehicle 102, at least a portion of visual scene 280 corresponding to a simulated event 285 that may be used for calibrating and/or testing one or more cameras of a vehicle 102, or a combination thereof. The display control system 790 can include a target and/or simulation event renderer 792. The target and/or simulation event renderer 792 can render the virtual camera targets 200H for display on the one or more displays 788. The target and/or simulation event renderer 792 can render the visual scene 280 corresponding to a simulated event 285 for display on the one or more displays 788. The display control system 790 can include a viewpoint renderer 794. The viewpoint renderer 794 can render at least portions of one or more the virtual camera targets 200H and/or visual scenes 280 for capture by a single viewport of a single sensor (e.g., a single camera) of the vehicle 102. In some cases, the viewpoint renderer 794 can distort and/or warp and/or move and/or crop and/or otherwise process portions of the virtual camera targets 200H and/or visual scenes 280 as rendered by the target and/or simulation event renderer 792. For instance, the viewpoint renderer 794 can use distortion correction based on distortion (e.g., radial distortion) expected from a particular lens of a particular camera of the vehicle. The viewpoint renderer 794 can use perspective distortion correction based on perspective distortion expected from a particular a particular camera of the vehicle based on a direction that the camera of the vehicle is pointing (e.g., the camera may be tilted up or down, which may affect how certain content should be displayed. The display control system 790 can include a display controller 795. Rendered content can be sent to the display controller 795, such as content generated by the target and/or simulation event renderer 792 and/or the viewpoint renderer 794, can be formatted for display on the one or more displays 788 by the display controller 795. The display control system 790 can include a computing device 796, which may be used to run elements such as the target and/or simulation event renderer 792, the viewpoint renderer 794, and/or the display controller 795. Some of these elements may be distinct hardware components. The display control system 790 can include one or more wired and/or wireless transceivers 798, through which the display control system 790 may interact with the vehicle 102, the dynamic scene control bridge 710, or any other element in the architecture 700.

The power supply system 702 may include batteries, generators, or may plug into an outlet and into the power grid. The power supply system 702 may supply power to the various elements and components of the system architecture 700, including at least the dynamic scene control bridge 710, the motorized turntable system 450, the lighting/heating system 760, the target control system 770, and the scene surveying system 610. The power supply system 702 may also charge the vehicle 102 before, during, and/or after calibration, for example if the vehicle 102 is electric or hybrid. The power supply system 702 may also charge batteries of mobile robots 788 and/or power other systems of the robotic scene control system 790. The power supply system 702 may also intelligently scale voltage, amperage, and current as appropriate for each element and component of the system architecture 700, and to do so it may include a computing system 1500. It may also include a wired and/or wireless transceiver (not pictured) through which it may interact with the vehicle 102, the dynamic scene control bridge 710, or any other element in the architecture 700.

The computing devices 110, 715, 745, 764, 776, 784, and 798 may each, at least in some cases, include at least one computing system 1500 as illustrated in or discussed with respect to FIG. 15 , or may include at least a subset of the components illustrated in FIG. 15 or discussed with respect to computing system 1500.

FIG. 8 illustrates operations 800 of a display control system 790 for rendering and displaying calibration targets and/or simulation scenes for display on one or more displays that are photographed by one or more cameras of a vehicle during a vehicle sensor calibration process and/or a vehicle sensor simulation process. In some examples, the operations 800 may be performed by a display control system 790.

At operation 805, the display control system 790 renders a simulated event. The simulated event may be based on a real event (e.g., based on prior camera data recorded at a previous time by a camera of a vehicle), may be computer-generated, or may be a combination thereof. The visual scene 280 corresponding to the simulated event 285 illustrated in FIGS. 2J, 5B, and 6D may be examples of the simulated event of operation 805.

At operation 810, the display control system 790 renders one or more virtual vehicle sensor calibration targets. The virtual vehicle sensor calibration targets can include planar targets, polygonal targets, three-dimensional targets, polygonal targets, or a combination thereof. The virtual camera calibration targets 200H of FIGS. 2H, 5A, and 6A-6C may be examples of the one or more virtual vehicle sensor calibration targets of operation 810. Operations 805 and/or 810 may be performed by the target and/or simulation event renderer 792.

At operation 815, the display control system 790 generates a virtual environment with the rendered content (e.g., the virtual vehicle sensor calibration targets and/or the simulated event). The virtual environment may be a virtual environment that includes the vehicle 102 and/or that is includes an area and/or volume around the vehicle 102. For example, the virtual targets may be arranged in particular poses relative to the vehicle 102 in the virtual environment. Similarly, elements in the simulated event (e.g., the visual scene 280) may be positioned and/or oriented relative to the vehicle 102 in the virtual environment. Operation 815 may be performed by the target and/or simulation event renderer 792.

At operation 820, the display control system 790 render vehicle sensor viewport-ready scene based on rendered content. Each vehicle sensor viewport-ready scene of operation 820 may be a view of the virtual environment of operation 815 from the simulated perspective of a particular one of the sensors 180 (e.g., a particular camera) of the vehicle 102. Each vehicle sensor viewport-ready scene can depict a simulated event of operation 805 (e.g., may include a visual scene 280), may depict the virtual camera calibration targets 200H of operation 810, or a combination thereof. In some cases, one or more of the vehicle sensor viewport-ready scenes of operation 820 may be processed, for example using perspective distortion correction, warping, distortion, movement, cropping, brightness adjustments, contrast adjustment, saturation adjustments, other image property adjustments, or combinations thereof. This processing may be performed in order to better simulate the perspective of a particular one of the sensors 180 of the vehicle 102. For instance, the viewpoint renderer 794 can apply a distortion correction (e.g., radial distortion correction) to a vehicle sensor viewport-ready scene based on distortion (e.g., radial distortion) expected from a particular lens of a particular camera of the vehicle. The viewport-ready scene may be processed so that the cameras of the vehicle 102 whose sensors (cameras) are being calibrated or otherwise tested (e.g., via simulation) can view as much of the scene as the cameras would if the scene was really around the vehicle 102. The viewpoint renderer 794 can use perspective distortion correction based on perspective distortion expected from a particular a particular camera of the vehicle based on a direction that the camera of the vehicle is pointing (e.g., the camera may be tilted up or down, which may affect how certain content should be displayed. Operation 820 can be performed by the target and/or simulation event renderer 792, the viewport renderer 794, or a combination thereof.

At operation 825, the display control system 790 can send one or more portions of viewport-ready scene to one or more display controllers 795 for display on one or more display screens 788. The target and/or simulation event renderer 792 and/or the viewport renderer 794 can send the viewport-ready scene to the display controller(s) 795. For instance, the target and/or simulation event renderer 792 and/or the viewport renderer 794 can send different portions of the viewport-ready scene to different display controllers 795. At operation 830, the display control system 790 can display the one or more portions of viewport-ready scene on one or more display screens 788. For instance, different portions of the viewport-ready scene can be displayed on different displays of the one or more displays 788 using the display controllers 795.

At operation 835, the vehicle sensors 180 of the vehicle 102 vehicle sensors capture sensor data depicting the rendered content (e.g., the virtual vehicle sensor calibration targets and/or the simulated event) as pictured in viewport-ready scene. At operation 840, the vehicle internal computing system 110 of the vehicle 102 outputs vehicle actions based on the simulated event. For example, the vehicle actions can include driving straight, turning left, turning right, accelerating, decelerating, maintaining speed, changing lanes, braking, swerving, turning on lights, turning off lights, turning on wipers, turning off wipers, honking the horn, and other driving actions. The vehicle internal computing system 110 can output the vehicle actions by having the vehicle actually perform the vehicle actions, by indicating (e.g., storing in a log and/or transmitting to another device or computer) what vehicle actions the vehicle 102 would perform in a given situation, or a combination thereof. The operations 930 (e.g., operations 955 and 960) of FIG. 9B provide examples of the operation 840. At operation 845, the vehicle computing system outputs calibrates vehicle sensors based on vehicle sensor calibration target(s). The operations 900 of FIG. 9A provide examples of the operation 845.

FIG. 9A illustrates vehicle operations 900 for sensor calibration. The operations 900 may be performed by a calibration device including a computing device coupled to one or more sensors in a housing. In some cases, the calibration device may include an internal computing device 110 of a vehicle 102 with sensors 180.

At operation 905, the calibration device receives a plurality of camera datasets from a camera coupled to a housing. The camera captures the plurality of camera datasets over the course of a calibration time period while the housing is in an analysis environment. Examples of the analysis environment include the hallway vehicle analysis environment 300, the dynamic scene vehicle analysis environment 400A, the dynamic scene vehicle analysis environment 400B, the dynamic scene vehicle analysis environment 500A, the dynamic scene vehicle analysis environment 500B, the dynamic scene vehicle analysis environment 600A, the dynamic scene vehicle analysis environment 600B, the dynamic scene vehicle analysis environment 600C, the dynamic scene vehicle analysis environment 600D, vehicle analysis environment whose system architecture 700 is illustrated in FIG. 7 , or a combination thereof. The calibration time period may be at least a portion of a time that the calibration device (e.g., the vehicle 102) spends inside the analysis environment. The plurality of camera datasets may capture portions of the virtual environment of operation 815, viewport-ready scene(s) of the virtual environment of operations 820-835, or a combination thereof.

In some examples, the housing is a vehicle. In some examples, the vehicle is an automobile. For instance, the housing may be the autonomous vehicle 102.

At operation 910, the calibration device identifies, within at least a subset of the plurality of camera datasets, a visual representation of a virtual sensor target displayed on one or more displays in the analysis environment. Examples of the one or more displays of operation 910 include the one or more displays 275 of FIGS. 2H, 2J, 5A, 5B, 6A, 6B, 6C, and 6D and/or the one or more displays 788 of FIG. 7 . Examples of the virtual sensor target of operation 910 can include the virtual camera calibration targets 200H of FIGS. 2H, 5A, and 6A-6C. Examples of the virtual sensor target of operation 910 can include at least portions of the visual scene 280 of FIGS. 2J, 5B, and 6D that are used as sensor targets. The virtual sensor target of operation 910 can be an example of the one or more virtual sensor calibration targets of operation 810. The visual representation of the virtual sensor target of operation 910 can be part of the virtual environment generated in operation 815 and captured in sensor data in operation 835. The visual representation of the virtual sensor target of operation 910 can be part of the viewport-ready scene(s) of the virtual environment of operations 820-835 and/or to other types of portions of the virtual environment of operation 815 as captured in sensor data in operation 835.

In some examples, the housing is rotated into a plurality of orientations by a motorized turntable 405 over the course of the calibration time period. The camera captures the plurality of camera datasets over the course of the calibration time period by capturing at least one of the plurality of sensor capture datasets while the housing is at each of the plurality of orientations. Examples of motorized turntable 405 used with virtual sensor targets displayed on one or more displays are illustrated in FIGS. 5A, 6A, 6B, and 7 .

In some examples, the virtual sensor target as displayed on the one or more displays moves along a plurality of display positions on the one or more displays over the course of the calibration time period. The camera captures the plurality of camera datasets over the course of the calibration time period by capturing at least one of the plurality of sensor capture datasets while the virtual sensor target is at each of the plurality of display positions on the one or more displays. In some examples, the movement of the virtual sensor target on the displays may simulate movement of the virtual sensor target that the camera would capture in response to rotation of the housing by a motorized turntable 405.

The one or more displays can include a curved display surface, for example as illustrated in FIGS. 5A, 5B, 6A, 6B, 6C, and 6D. The one or more displays can include a display screen wall that displays at least the virtual sensor target, for example as illustrated in FIGS. 3, 5A, 5B, 6A, 6B, 6C, and 6D. The one or more displays can include a projection surface and a projector that projects at least the virtual sensor target onto the projection surface.

In some examples, the virtual sensor target includes at least one virtual polygonal surface. For instance, the virtual sensor target can be a planar (e.g., 2D) surface. The planar surface can be curved in some cases. The virtual sensor target can be a 3D shape, such as a polygon, a regular polyhedron, an irregular polyhedron, a rounded 3D shape, a curved 3D shape, or a combination thereof. For example, the display(s) 275 of FIG. 5A display four planar virtual sensor targets, respectively simulating two checkerboard camera calibration patterns 200A of FIG. 2A at different poses, a combined sensor target 250 of FIG. 2F, and a dot lattice camera calibration pattern 200D of FIG. 2D. The display(s) 275 of FIG. 5A also display one virtual sensor target having a three-dimensional shape, simulating a polyhedral combined sensor target 255 of FIG. 2G.

At operation 915, the calibration device identifies a virtual pose of the virtual sensor target as depicted in the visual representation of the virtual sensor target. The pose of the virtual sensor target can include a position of the virtual sensor target and/or an orientation of the virtual sensor target. For example, a pose of the virtual sensor target can include the horizontal position of the virtual sensor target on the one or more displays, the vertical position of the virtual sensor target on the one or more displays, the simulated depth (e.g., simulated distance in a depth direction orthogonal to a surface of the one or more displays) of the virtual sensor target, the pitch of the virtual sensor target, the yaw of the virtual sensor target, the roll of the virtual sensor target, or a combination thereof. The virtual pose of the virtual sensor target of operation 915 can be an example of a pose of one of the virtual sensor calibration targets of operation 810 within the virtual environment of operation 815, in some cases as processed for the viewport-ready scene(s) of the virtual environment of operations 820-835.

In some examples, identifying the virtual pose of the virtual sensor target includes receiving the virtual pose of the virtual sensor target from a rendering device (e.g., the display control system 790 that renders the virtual sensor target in the virtual pose for display on the one or more displays.

In some examples, identifying the virtual pose of the virtual sensor target is based on identifying a perspective distortion of a pattern in the visual representation of the virtual sensor target. The virtual sensor target includes the pattern. The pattern may be any pattern discussed herein, such as a checkerboard pattern 210A/210G, an ArUco pattern 210B, a crosshair pattern 210C, a dot lattice pattern 210D, a pattern with circular markings 230, a pattern with target ID markings 235, a bar code pattern, a quick response (QR) code pattern, an Aztec code pattern, a semacode pattern, a data matrix code pattern, a PDF417 code pattern, a MaxiCode pattern, a shotcode pattern, a HCCB pattern, a pattern modeled after a human body, a pattern modeled after a human face, or a combination thereof. For instance, in the example of FIG. 5A, the calibration device can determine, based on the perspective distortion of the dot matrix pattern, that the virtual sensor target corresponding to the dot matrix pattern is rotated clockwise about an axis orthogonal to the surface of the display(s) 275 of FIG. 5A, is rotated slightly left about a vertical axis, is located in the top-right corner of the display(s) 275 of FIG. 5A, and is simulated to be fairly close to the housing (the vehicle 102 on the turntable 405 in FIG. 5A because the dot pattern is large as displayed on the display(s) 275 of FIG. 5A. This pose may be determined, for instance, based on comparison of the visual representation of the virtual sensor target in at least the subset of the plurality of camera datasets to a known un-distorted version of the pattern (e.g., as the pattern would appear viewed from an approach vector orthogonal to the pattern).

At operation 920, the calibration device calibrates interpretation of data captured by the camera based on the visual representation of the virtual sensor target and based on the virtual pose of the virtual sensor target as depicted in the visual representation of the virtual sensor target. In some examples, calibrating interpretation of data captured by the camera includes generating a transformation mapping a position associated with the visual representation of the virtual sensor target to a position in the analysis environment. Calibrating interpretation of data captured by the camera based on the visual representation of the virtual sensor target and based on the virtual pose of the virtual sensor target as depicted in the visual representation of the virtual sensor target as in operation 920 can be an example of a vehicle computing system calibrating vehicle sensors based on vehicle sensor calibration target(s) as in operation 845.

In some examples, the calibration device receives a post-calibration camera dataset from the camera while the housing is in an external position outside of the analysis environment. The calibration device identifies a representation of an object within a scene within the post-calibration camera dataset. The calibration device identifies a position of the object relative to the external position of the housing by applying the transformation to a position of the representation of the object within the scene.

FIG. 9B illustrates vehicle operations for running a vehicle through a simulation. The operations 930 may be performed by a simulation device including a computing device coupled to one or more sensors in a housing. In some cases, the simulation device may include an internal computing device 110 of a vehicle 102 with sensors 180.

At operation 935, the simulation device receives a plurality of camera datasets from a camera coupled to a housing, wherein the camera captures the plurality of camera datasets over the course of a simulation time period while the housing is in an analysis environment. Examples of the analysis environment include the hallway vehicle analysis environment 300, the dynamic scene vehicle analysis environment 400A, the dynamic scene vehicle analysis environment 400B, the dynamic scene vehicle analysis environment 500A, the dynamic scene vehicle analysis environment 500B, the dynamic scene vehicle analysis environment 600A, the dynamic scene vehicle analysis environment 600B, the dynamic scene vehicle analysis environment 600C, the dynamic scene vehicle analysis environment 600D, vehicle analysis environment whose system architecture 700 is illustrated in FIG. 7 , or a combination thereof. The calibration time period may be at least a portion of a time that the calibration device (e.g., the vehicle 102) spends inside the analysis environment. The plurality of camera datasets may capture portions of the virtual environment of operation 815, viewport-ready scene(s) of the virtual environment of operations 820-835, or a combination thereof.

In some examples, the housing is a vehicle. In some examples, the vehicle is an automobile. For instance, the housing may be the autonomous vehicle 102.

At operation 940, the simulation device identifies, within at least a subset of the plurality of camera datasets, visual representations of one or more portions of a virtual environment displayed on one or more display screens in the analysis environment. The visual scenes 280 corresponding to the simulated events 285 illustrated in FIGS. 2J, 5B, and 6D are examples of at least portions of a virtual environment in the context of operation 940. The virtual environment may be based on previously-recorded camera sensor data, based on computer-generated data (e.g., not from past camera data), or a combination thereof. The virtual environment of operation 940 may be the virtual environment of operation 815. The one or more portions of the virtual environment of operation 940 may correspond to the viewport-ready scene(s) of the virtual environment of operations 820-835 and/or to other types of portions of the virtual environment of operation 815.

In some examples, the housing is rotated into a plurality of orientations by a motorized turntable 405 over the course of the simulation time period. The camera captures the plurality of camera datasets over the course of the simulation time period by capturing at least one of the plurality of sensor capture datasets while the housing is at each of the plurality of orientations. Examples of motorized turntable 405 used with one or more displays are illustrated in FIGS. 5A, 6A, 6B, and 7 .

In some examples, the virtual environment as displayed on the one or more displays moves along a plurality of display positions on the one or more displays over the course of the simulation time period. The camera captures the plurality of camera datasets over the course of the simulation time period by capturing at least one of the plurality of sensor capture datasets while the virtual environment is rendered and/or displayed at each of the plurality of display positions and/or display orientations on the one or more displays. In some examples, the movement of the virtual sensor target on the displays may simulate movement of the virtual environment that the camera would capture in response to rotation of the housing by a motorized turntable 405. In some examples, the movement of the virtual sensor target on the displays may simulate movement of the virtual environment that the camera would capture in response to the actuated movement of the housing (e.g., driving of a vehicle, where the housing is the vehicle).

The one or more displays can include a curved display surface, for example as illustrated in FIGS. 5A, 5B, 6A, 6B, 6C, and 6D. The one or more displays can include a display screen wall that displays at least the virtual environment, for example as illustrated in FIGS. 3, 5A, 5B, 6A, 6B, 6C, and 6D. The one or more displays can include a projection surface and a projector that projects at least the virtual sensor target onto the projection surface.

At operation 945, the simulation device generates a mapped representation of the virtual environment based on the visual representations of one or more portions of the virtual environment. The mapped representation of the virtual environment may identify the position of the housing relative to other objects in the virtual environment, such as roads, vehicles, pedestrians, bicyclists, traffic lights, trees, buildings, plants, other structures, road hazards, barriers, and other objects. The mapped representation of the virtual environment may include a 3D representation of the virtual environment. The mapped representation of the virtual environment may include one or more 2D representations of the virtual environment, for example representing one or more top-down views of the virtual environment, each corresponding to different heights. The mapped representation of the virtual environment may ignore certain objects in the virtual environment, for example if such objects do not affect driving (if the housing is a vehicle).

At operation 950, the simulation device identifies an event occurring within the virtual environment over the course of the simulation time period based on the mapped representation of the virtual environment. The event, for example, include a traffic accident, a vehicular accident, a collision, an accident involving a collision with the housing, an accident involving a collision of other objects, another type of accident, a traffic jam, a hazard on the road, a pedestrian walking into a dangerous area, a dangerous movement by another vehicle, a sharp turn by another vehicle, a sharp acceleration by another vehicle, a sharp deceleration by another vehicle, a lane change by another vehicle, another vehicle coming within a threshold distance (e.g., at least partially virtual distance based on the mapped representation of the virtual environment) of the housing, a pedestrian coming within a threshold distance (e.g., at least partially virtual distance based on the mapped representation of the virtual environment) of the housing, another object coming within a threshold distance (e.g., at least partially virtual distance based on the mapped representation of the virtual environment) of the housing, an event requiring a sharp turn to avoid an accident, an event requiring a sharp acceleration to avoid an accident, an event requiring a sharp deceleration to avoid an accident, an event requiring a lane change to avoid an accident, another event discussed herein, another traffic event that may be useful for testing an autonomous vehicle 102, or a combination thereof. The simulated event of operation 950 may be an example of the simulated event of operation 805.

At operation 955, the simulation device identifies one or more actions to be taken by one or more actuators coupled to the housing in response to the event occurring within the virtual environment. At operation 960, the simulation device outputs the one or more actions. For example, if the housing is a vehicle 102, then the simulation device includes the vehicle internal computing system 110. The simulation device (e.g., the vehicle internal computing system 110 of the vehicle 102) can output the one or more actions based on the event identified in the virtual environment. For example, the one or more actions can include driving straight, turning left, turning right, accelerating, decelerating, maintaining speed, changing lanes, braking, swerving, turning on lights, turning off lights, adjusting light brightness, opening windows, closing windows, opening doors, closing doors, unlocking doors, locking doors, adjusting a climate control, adjusting a radio, adjusting an audio system, turning on wipers, turning off wipers, honking the horn, and other driving-related actions. In some examples, to output the one or more actions, the one or more processors output signals targeted to other components of the housing that are configured to, and can, execute the actions. In an illustrative example, the vehicle internal computing system 110 can output the vehicle actions by having the vehicle actually perform the vehicle actions. For instance, the vehicle internal computing system 110 can identify signals that the vehicle internal computing system 110 targeted toward other components of the vehicle 102 (e.g., a steering system, a braking system, an acceleration system, a deceleration system, a computer vision system, a window control system, a light control system, a climate control system, a door control system, a wiper control system, a horn control system, a radio, an audio system, a wiper system, or a combination thereof). The signals would cause these components of the vehicle 102 to execute these actions if the vehicle 102 was in a real-world driving scenario and not a simulation scenario. In some cases, the vehicle 102 may be allowed to execute some of these actions depending on the vehicle analysis environment. In some examples, the vehicle 102 may be allowed to execute actions related to lighting, windows, horn, radio, brakes, and turning, but not acceleration (e.g., if the vehicle analysis environment does not include room for the vehicle 102 to drive). In some examples, the vehicle 102 may be allowed to execute any and all actions, for instance, if the vehicle analysis environment does include room for the vehicle 102 to drive and/or a way for the vehicle 102 to move its wheels without actually accelerating, as in the hallway vehicle analysis environment 300, a vehicle analysis environment with a treadmill (not pictured), a chassis dynamometer (not pictured), a raised platform (not pictured) raising the vehicle 102, or a combination thereof. The internal computing system 110 can indicate (e.g., storing in a log and/or transmitting to another device or computer) what vehicle actions the vehicle 102 would perform in the situation of the event identified in the virtual environment. For example, if the event identified in the virtual environment in operation 950 includes a hazard immediately in front of the vehicle (e.g., an accident, a pedestrian unexpectedly running into the roadway, another vehicle making an unexpected move), then the one or more actions can include hard braking to avoid colliding with the hazard, swerving left or right to avoid the hazard, and/or honking the horn to alert drivers of other vehicles and/or at-risk pedestrians about the hazardous situation. The one or more actions of operations 955-960 may be examples of the vehicle actions of operation 840.

FIG. 10 is a flow diagram illustrating operation of a calibration environment. In particular, FIG. 10 illustrates a process 1000.

At operation 1005, a high resolution map of calibration environment is generated. This may be performed using the scene surveying system 610, for example. Examples of the calibration environment may include the hallway vehicle analysis environment 300, the dynamic scene vehicle analysis environment 400A, the dynamic scene vehicle analysis environment 400B, the dynamic scene vehicle analysis environment 500A, the dynamic scene vehicle analysis environment 500B, the dynamic scene vehicle analysis environment 600A, the dynamic scene vehicle analysis environment 600B, the dynamic scene vehicle analysis environment 600C, the dynamic scene vehicle analysis environment 600D, vehicle analysis environment whose system architecture 700 is illustrated in FIG. 7 , or a combination thereof.

At operation 1010, the sensors 180 on the vehicle 102 are run in the calibration environment. In some examples, the sensors 180 on the vehicle 102 can be run periodically at different rotation positions of the vehicle 102, which is rotated using a motorized turntable 405. In some examples, the sensors 180 on the vehicle 102 can be run periodically when virtual sensor targets (e.g., virtual camera calibration targets 200H) are displayed at different display positions on display(s) 275/788.

At operation 1015, the vehicle 102 generates a calibration scene based on its sensors 180, based on (a) synchronized sensor data, (b) initial calibration information, (c) vehicle pose information, and (d) target locations. In some examples, the turntable 405 of operation 1010 may be omitted. Instead, rotation of content displayed on one or more displays 275/788 around the vehicle 102 may simulate rotation of the vehicle 102 about a turntable 405.

At operation 1015, the calibration systems in the vehicle read the calibration scene and: (a) detect targets in each sensor frame, (b) associate detected targets, (c) generate residuals, (d) solve calibration optimization problem, (e) validate calibration optimization solution, and (f) output calibration results. At operation 1025, the calibration results are tested against acceptable bounds and checked for numerical sensitivity. Successful calibration measurements are stored and logged, along with a minimal subset of data needed to reproduce them.

FIG. 11 is a flow diagram illustrating operations for intrinsic calibration of a vehicle sensor using a dynamic scene. In particular, FIG. 11 illustrates a process 1100.

At operation 1105, a vehicle 102 is rotated into a plurality of vehicle positions over a course of a calibration time period using a motorized turntable 405. The vehicle 102 and motorized turntable 405 are located in a calibration environment. Examples of the calibration environment may include the he hallway vehicle analysis environment 300, the dynamic scene vehicle analysis environment 400A, the dynamic scene vehicle analysis environment 400B, the dynamic scene vehicle analysis environment 500A, the dynamic scene vehicle analysis environment 500B, the dynamic scene vehicle analysis environment 600A, the dynamic scene vehicle analysis environment 600B, the dynamic scene vehicle analysis environment 600C, the dynamic scene vehicle analysis environment 600D, vehicle analysis environment whose system architecture 700 is illustrated in FIG. 7 , or a combination thereof.

At operation 1110, the vehicle 102 captures a plurality of sensor capture datasets via a sensor coupled to the vehicle over the course of the calibration time period. In some examples, the vehicle 102 captures the plurality of sensor capture datasets by capturing at least one of the plurality of sensor capture datasets while the vehicle is at each of the plurality of vehicle positions. In some examples, the vehicle 102 captures the plurality of sensor capture datasets by capturing at least one of the plurality of sensor capture datasets while the display(s) 275/788 are displaying virtual sensor targets (e.g., virtual camera calibration targets 200H) in each of a set of display positions along the display(s) 275/788.

At operation 1115, an internal computing system 110 of the vehicle 102 receives the plurality of sensor capture datasets from the sensor coupled to the vehicle over a course of a calibration time period. At operation 1120, the internal computing system 110 of the vehicle 102 identifies, in the plurality of sensor capture datasets, one or more representations of (at least portions of) the calibration environment that include representations of a plurality of sensor targets. The plurality of sensor targets are located at known (i.e., previously stored) positions in the calibration environment. At operations 1125-1130, the sensor is calibrated based on the representations of a plurality of sensor targets identified in the plurality of sensor capture datasets.

More specifically, at operation 1125, the internal computing system 110 of the vehicle 102 identifies positions of the representations of the plurality of sensor targets within the one or more representations of (at least portions of) the calibration environment. If the sensor being calibrated is a visual light or infrared camera, and the one or more representations of (portions of) the calibration environment are images, then the representations of the sensor targets may be areas within the one or more images comprised of multiple pixels, which the computing system 110 of the vehicle 102 can identify within the one or more images by generating high-contrast versions of the one or more images (i.e., “edge” images) that are optionally filtered to emphasize edges within the image, and by identifying features within the image comprised of one or more of those edges, the features recognizable as portions of the target. For example, the vertices and/or boxes in the checkerboard pattern 210A or the ArUco pattern 210B or pattern 210G, curves or vertices in the crosshair pattern 210C, the circular ring marking patterns 230 or dot patterns 210D, or combinations thereof, may each be visually recognized as features in this way. If the camera is an infrared camera, these edges may correspond to edges between hotter/warmer areas (that have absorbed more heat) and cooler/colder areas (that have absorbed less heat). The hotter areas may be, for example, dark markings after absorbing heat from a heat source 625, while the colder areas may be the substrate 205, which does not absorb as much heat from the heat source 625.

Similarly, if the sensor being calibrated is a radar sensor, the radar sensor may recognize the trihedral shape 215 of the target 220 as a feature due to its reflective pattern that results in a high radar cross section (RCS) return. Similarly, if the sensor being calibrated is a lidar sensor, the lidar sensor may recognize the surface of the substrate 205 of the target 250 and the apertures 225/240 within the substrate 205 of the target 250, which may be recognized as a feature due to the sharp changes in range/depth at the aperture.

At operation 1130, the internal computing system 110 of the vehicle 102 generates a transformation that maps (1) the positions of the representations of the plurality of sensor targets within one or more representations of (portions of) the calibration environment to (2) the known positions of the plurality of sensor targets within the calibration environment. Other information about the plurality of sensor targets, such as information storing visual patterns or aperture patterns of the sensor targets, may also be used to generate the transformation. For example, if the sensor being calibrated is a camera, and the computing device 110 knows that an image should have a checkerboard pattern 210A of a sensor target 200A, and recognizes a warped or distorted variant of the checkerboard pattern 210A (e.g., because the camera includes a fisheye lens or wide-angle lens), then the computing device 110 may use its knowledge of the way that the checkerboard should look, such as how far the vertices are from each other, that they should form squares, and that the squares are arranged in a grid pattern—to generate a transformation that undoes the distortion caused by the camera, thereby mapping the vertices detected in the image to real-world positions, at least relative to one another. In other words, the transformation includes one or more projective transformations of various 2-D image coordinates of sensor target features into 3-D coordinates in the real world and optionally back into 2-D image coordinates that have been corrected to remove distortion and/or other sensor issues.

Because the computing device 110 knows ahead of time exactly where the sensor targets are in the calibration environment, the transformation may also map the positions of the vertices in the image (and therefore the positions of the representations of the sensor targets in the representation of the calibration environment) to real-world positions in the calibration environment. The transformation(s) that are generated during intrinsic sensor calibration at operation 1130 can include one or more types of transformations, including translations, stretching, squeezing, rotations, shearing, reflections, perspective distortion, distortion, orthogonal projection, perspective projection, curvature mapping, surface mapping, inversions, linear transformations, affine transformations, The translational and rotational transformations may include modifications to position, angle, roll, pitch, yaw, or combinations thereof In some cases, specific distortions may be performed or undone, for example by removing distortion caused by use of a specific type of lens in a camera or other sensor, such as a wide-angle lens or a fisheye lens or a macro lens.

Step 1130 may be followed by step 1105 and/or by step 1110 if calibration is not yet complete, leading to gathering of more sensor capture datasets and further refinement of the transformation generated at operation 1130. Step 1130 may alternately be followed by step 1145.

The previously stored information about the plurality of sensor targets may be from a high-definition map generated as in step 1005 of FIG. 10 , may be from a second sensor on the vehicle, or may simply be based on a prior understanding of the sensor targets. For example, the internal computing system 110 of the vehicle 102 understands what a checkerboard pattern 210A is and that the grid it forms ought to look include a grid of parallel and perpendicular lines under normal conditions. Because of this, the internal computing system 110 understands that if the representation it received from the sensor (camera) of a target with a checkerboard pattern 210A forms a grid warped or distorted by a wide-angle lens or fisheye lens, this difference (step 1130) can be corrected by the internal computing system 110 by correctively distorting or warping the image captured by the sensor (camera) by to reverse the warping or distortion in the representation until the corrected checkerboard looks like it should. This corrective warping or distortion is the correction generated in step 1135. The correction may also include a translation along X, Y, or Z dimensions, a rotation along any axis, a warp or distortion filter, a different type of filter, or some combination thereof.

Steps 1145-1160 concern operations that occur after calibration is complete (i.e., post-calibration operations). At operation 1145, the sensor of the vehicle captures a post-calibration sensor capture dataset after the calibration time period, after generating the transformation, and while the vehicle is in a second position that is not in the calibration environment. At operation 1150, the computing device 110 of the vehicle 102 identifies a representation of an object within a representation of a scene identified within the post-calibration sensor capture dataset. At operation 1155, the computing device 110 of the vehicle 102 identifies a position of the representation of the object within the representation of the scene. At operation 1160, the computing device 110 of the vehicle 102 identifies a position of the object relative to the second position of the vehicle by applying the transformation to the position of the representation of the object within the representation of the scene.

Note that capture of data by the sensors 180 of the vehicle 102 may occur in parallel with calibration of the sensors 180 of the vehicle 102. While an initial correction is generated at operation 1135, the vehicle 102 may continue to rotate, and its sensors 180 may continue to capture more sensor data, hence the dashed lines extending back up to steps 1105 and 1110 from step 1135. When step 1135 is reached a second, third, or N^(th) time (where N is any integer over 1), the correction generated the first time step 1135 was reached may be updated, revised, and/or re-generated based on the newly captured sensor data when step 1135 is reached again. Thus, the correction becomes more accurate as calibration continues.

For some additional context on intrinsic calibration: LIDAR intrinsic properties may include elevation, azimuth, and intensity. Camera intrinsic properties may be given as matrices based on camera region/bin, and may track projection, distortion, and rectification. All sensors' intrinsic properties (including LIDAR and camera) may include position in X, Y, and/or Z dimensions, as well as roll, pitch, and/or yaw.

In some examples, the turntable 405 of operation 1105 may be omitted. Instead, rotation of content displayed on one or more displays 275/788 around the vehicle 102 may simulate rotation of the vehicle 102 about a turntable 405. Instead of different positions of the vehicle 102, the different sensor capture datasets are captured (at operations 1110 and 1115) when content (e.g., virtual camera targets 200H) that is displayed on one or more displays 275/788 is at different positions. The known positions in the calibration environment of operations 1120 and 1130 may be known display positions of the content as displayed on the one or more displays 275/788 relative to the one or more displays 275/788, relative to the calibration environment, relative to the vehicle, or some combination thereof.

FIG. 12 is a flow diagram illustrating operations for extrinsic calibration of two sensors in relation to each other using a dynamic scene. In particular, FIG. 12 illustrates a process 1200.

At operation 1205, a vehicle 102 is rotated into a plurality of vehicle positions over a course of a calibration time period using a motorized turntable 405. The vehicle 102 and the turntable 405 may be in a calibration environment, such as the hallway vehicle analysis environment 300, the dynamic scene vehicle analysis environment 400A, the dynamic scene vehicle analysis environment 400B, the dynamic scene vehicle analysis environment 500A, the dynamic scene vehicle analysis environment 500B, the dynamic scene vehicle analysis environment 600A, the dynamic scene vehicle analysis environment 600B, the dynamic scene vehicle analysis environment 600C, the dynamic scene vehicle analysis environment 600D, vehicle analysis environment whose system architecture 700 is illustrated in FIG. 7 , or a combination thereof.

At operation 1210, the vehicle 102 captures a first plurality of sensor capture datasets via a first sensor coupled to the vehicle over the course of the calibration time period. In some examples, the vehicle 102 captures the first plurality of sensor capture datasets via the first sensor by capturing at least one of the first plurality of sensor capture datasets while the vehicle 102 is at each of the plurality of vehicle positions. In some examples, the vehicle 102 captures the first plurality of sensor capture datasets via the first sensor by capturing at least one of the first plurality of sensor capture datasets while the display(s) 275/788 are displaying virtual sensor targets (e.g., virtual camera calibration targets 200H) in each of a set of display positions along the display(s) 275/788.

At operation 1215, the vehicle 102 captures a second plurality of sensor capture datasets via a second sensor coupled to the vehicle over the course of the calibration time period. In some examples, the vehicle 102 captures the second plurality of sensor capture datasets via the second sensor by capturing at least one of the second plurality of sensor capture datasets while the vehicle 102 is at each of the plurality of vehicle positions. In some examples, the vehicle 102 captures the second plurality of sensor capture datasets via the second sensor by capturing at least one of the second plurality of sensor capture datasets while the display(s) 275/788 are displaying virtual sensor targets (e.g., virtual camera calibration targets 200H) in each of a set of display positions along the display(s) 275/788. Either of operations 1210 and 1215 can occur first (e.g., at least partially before the other). In some examples, operations 1210 and 1215 can occur at least partially in parallel.

At operation 1220, the internal computing system 110 of the vehicle 102 receives the first plurality of sensor capture datasets from the first sensor and the second plurality of sensor capture datasets from the second sensor. At operation 1225, the internal computing system 110 of the vehicle 102 identifies, in the first plurality of sensor capture datasets, representations of a first plurality of sensor target features, the first plurality of sensor target features detectable by the first sensor due to a type of the first plurality of sensor target features being detectable by sensors of a type of the first sensor. At operation 1230, the internal computing system 110 of the vehicle 102 identifies, in the second plurality of sensor capture datasets, representations of a second plurality of sensor target features, the second plurality of sensor target features detectable by the second sensor due to a type of the second plurality of sensor target features being detectable by sensors of a type of the second sensor. Either of steps 1225 and 1230 can occur first, or they can occur at least partially in parallel.

The first plurality of sensor target features and the second plurality of sensor target features may be on the same targets; for example, if the first sensor is a camera, and the second sensor is a LIDAR sensor, and plurality of sensor targets are the combined extrinsic calibration targets 250 of FIGS. 2F and 5A, then the first plurality of sensor target features may be the visual markings (rings) 230 detectable by the camera, while the second plurality of sensor target features are the apertures 225 detectable by the LIDAR. Alternately, the plurality of sensor target features may be on different targets; for example, the first sensor may be a radar sensor and the first plurality of sensor target features may be the trihedral radar calibration targets 220, while the second sensor may be any other sensor (e.g., camera, lidar) and the second plurality of sensor target features may be, for example, the visual markings (rings) 230 or apertures 225 of the combined extrinsic calibration targets 250, or a pattern 210 of a camera intrinsic target such as the targets 200A-D, or any other target described herein. If the first sensor is a visual camera, and the second sensor is an distance measurement sensor, and plurality of sensor targets are the polyhedral sensor targets 255 of FIGS. 2G and 5B, then the first plurality of sensor target features may be the vertices 270 detectable by the camera, while the second plurality of sensor target features are also the vertices 270 but as detectable by the distance measurement sensor (e.g., LIDAR sensor). Note that the first sensor and the second sensor may be any combination of the possible types of sensors 180 discussed herein.

At operation 1235, the internal computing system 110 of the vehicle 102 compares the relative positioning of the representations of the first plurality of sensor target features and the representations of the second plurality of sensor target features to known relative positioning of the first plurality of sensor target features and the second plurality of sensor target features. In some cases, the relative positioning may be determined based on comparison of a position of a particular point in one representation, such as the center, to a particular point in the another representation to which it is being compared, such as the center. Points that can be used instead of the center may include or the highest point, lowest point, leftmost point, rightmost point, a point that is centered along one axis but not another, a point at the widest portion of the representation, a point at the narrowest portion of the representation, a point at a particular edge or vertex, or some combination thereof. At operation 1240, the internal computing system 110 of the vehicle 102 generates a transformation based on the comparison, such that the transformation aligns a first location identified by the first sensor and a second location identified by the second sensor.

As a first example, the first sensor may be a camera and the second sensor may be a LIDAR sensor, and the first plurality of sensor target features and the second plurality of sensor target features may both be features of the combined extrinsic calibration targets 250 of FIGS. 2F and 5A such that the first plurality of sensor target features are the visual markings (rings) 230 detectable by the camera and the second plurality of sensor target features are the apertures 225 detectable by the LIDAR. In such a case, the internal computing system 110 of the vehicle 102 identifies a center of a particular aperture 225 based on the LIDAR data, and identifies a center of a ring 230 based on the camera data, compares these at operation 1235 and identifies a relative distance between the two locations based on the internal computing system 110′s current geographic understanding of the calibration environment. Because the internal computing system 110 understands that these two points should represent the same location in the real world (i.e., their relative positioning indicates no distance between them), the internal computing system 110 generates a transformation—which may include, for example, a translation along X, Y, and/or Z dimensions, a rotation along any axis, a warp or distortion filter, or some combination thereof—that aligns these location points. That is, the transformation translates (1) a mapping of a point from the one sensor's capture data set to a real world position into (2) a mapping of a point from the other sensor's capture data set to the same real world position. While, with just a pair or two of such points, there may be multiple possible transformations that can perform this alignment, the internal computing system 110 can generate a transformation that works consistently for an increasing number of pairs such sets of points—for example, for each aperture 225 and ring 230 combinations of a target 250, and for each target 250 in the calibration environment. As the number of pairs increases, the number of possible transformations that can successfully align these. Different sensors may map the world around them differently; for example, if the camera includes a wide-angle lens while the other sensor (e.g., LIDAR) does not include an analogous distortion effect, the transformation may include some radial movement or other compensation for distortion.

As a second example, the first sensor may be a radar sensor and the second sensor may be a LIDAR sensor, and the first plurality of sensor target features may be trihedral radar calibration targets 220 while the second plurality of sensor target features may be apertures 225 of a combined target 250 or the planar boundaries of a substrate 205 of a camera target 200, each of which is a known distance away from the nearest trihedral radar calibration targets 220. In such a case, the internal computing system 110 of the vehicle 102 identifies a location of the trihedral radar calibration targets 220 based on radar sensor data and a location of the LIDAR target feature based on LIDAR sensor data, compares these at operation 1235 and identifies a relative distance between the two locations based on the internal computing system 110's current geographic understanding of the calibration environment. Because the internal computing system 110 understands that these two points should be a known distance away in a particular direction at a particular angle in the real world, the internal computing system 110 generates a transformation—which may include, for example, a translation along X, Y, and/or Z dimensions, a rotation along any axis, a warp or distortion filter, or some combination thereof—that aligns these location points to match the same known distance away in the particular direction at the particular angle as in the real world. While initially there may be multiple possible transformation that can perform this, the internal computing system 110 can generate a transformation that works consistently for multiple such sets of points—for example, for each trihedral radar calibration target 220 and each nearby LIDAR target feature pair in the calibration environment.

As a third example, the first sensor may be a visual camera and the second sensor may be an infrared sensor (e.g., an infrared camera). The first plurality of sensor target features may be dark markings as visually detectable by the camera, while the second plurality of sensor target features are the heat absorbed by the dark markings detectable by the infrared sensor (e.g., infrared camera). The positions of the dark markings themselves as visually recognizable should be the same or similar to the positioning of the heat absorbed by the dark markings detectable by the infrared sensor, so the center of each can be found and compared. Because the internal computing system 110 understands that these two points should represent the same location in the real world (i.e., their relative positioning indicates no distance between them), the internal computing system 110 generates a transformation—which may include, for example, a translation along X, Y, and/or Z dimensions, a rotation along any axis, a warp or distortion filter, or some combination thereof—that aligns these location points.

At operation 1245, the internal computing system 110 of the vehicle 102 receives, from the first sensor and second sensor, post-calibration sensor capture datasets captured by the first sensor and second sensor after the calibration time period. At operation 1250, the internal computing system 110 of the vehicle 102 applies the transformation generated in step 1240 to one or both of the post-calibration sensor capture datasets. For example, a representation of a particular object can be identified in a post-calibration sensor capture dataset captured by one sensor after calibration, and the transformation can be applied to find the same object within another post-calibration sensor capture dataset captured by another sensor after calibration. A real-world position of the same object may be found relative to the vehicle 102 based on intrinsic calibration of at least one of the two sensors and/or based on the transformation. In some cases, a representation of an entire space—that is, a three-dimensional volume—in one post-calibration sensor capture dataset captured by one sensor after calibration may then be identified in another post-calibration sensor capture dataset captured by another sensor by applying the transformation to multiple points within the space. Important points, such as vertices (e.g., corners of a room), edges (e.g., edges of a room), or other features may be selected as at least some of these points. With two aligned representations of a 3-D space, objects can be identified around the vehicle that might not otherwise be. For example, a pedestrian wearing all black might not visually stand out against (e.g., contrast against) a background of an asphalt road at night, but a RADAR or LIDAR might easily identify the pedestrian, and the transformation will still allow the computer 110 of the vehicle 102 to understand where that pedestrian is in its camera footage, allowing the vehicle to pay close attention to visual cues from the pedestrian that the RADAR or LIDAR might not catch or understand, such as presence or lack of a pet or small child accompanying the pedestrian. Developing the vehicle's understanding of its surroundings by aligning real-world (and relative) mappings of the inputs it receives from its sensors can save lives in the field of autonomous vehicles by allowing the best aspects of multiple sensors to complement one another to develop a comprehensive view of the vehicle's surroundings. No sensor is perfect at detecting everything—distance measurement sensors can see range/depth but not color or brightness, and can have trouble seeing small or fast-moving objects—while cameras can see color and brightness and visual features but can have trouble with depth perception. Thus, each sensor has its strengths, and the alignment made possible by the extrinsic calibration processes discussed in FIG. 12 can allow the best aspects of each sensor (the “pros” of each sensor) to complement each other and compensate for the downsides of each sensor (the “cons” of each sensor). Note that capture of data by the sensors 180 of the vehicle 102 may occur in parallel with calibration of the sensors 180 of the vehicle 102. While an initial transformation is generated at operation 1240, the vehicle 112 may continue to rotate, and its sensors 180 may continue to capture more sensor data, hence the dashed lines extending back up to steps 1205 and 1210 and 1215 from step 1240. When step 1240 is reached a second, third, or N^(th) time (where N is any integer over 1), the transformation generated the first time step 1240 was reached may be updated, revised, and/or re-generated based on the newly captured sensor data when step 1240 is reached again. Thus, the transformation becomes more accurate as calibration continues.

For some additional context on extrinsic calibration, all sensors' extrinsic properties may include relative positions in X, Y, and/or Z dimensions, as well as roll, pitch, and/or yaw. Target and vehicle locations are ground truthed via the mapping system discussed in step 1010 and further as discussed with respect to the transformation of step 1130 of FIG. 11 . Sensors of the vehicle 102 and scene surveying system 610 record the scene and each target is detected using a target detection method specific to that sensor and target pair. The measured target location is compared against the mapped target location to derive the total extrinsic sensor error:

Extr_(sensor)(R, t)=Σ_(target) ∥RC _(target) +t−D _(target) ∥²

Where C_(target) is the measured location of the target and D_(target) is the mapped location of the target. We can collect the intrinsic sensor calibration data (as in FIG. 11 ) in a similar way, at each frame of recorded data the targets are detected and intrinsics are collected. These intrinsic sensor calibration data (as in FIG. 11 ) might be the measured distance between pixel coordinates and the lines on a target, or lidar point coordinates and detected planar sides of a target. The total error for a single sensor can be summarized as:

ExtrIntr(R, t, α)_(sensor)=Intr_(sensor)(α)+Γ_(sensor) Extr_(sensor)(R, t)

The weight Γ_(sensor) determines the contribution of that sensor's extrinsic parameter. By collecting the Extrintr for every sensor we define a global cost function that describes all intrinsics and extrinsics in the system. We can minimize the total expected error by toggling the calibration parameters for each sensor [R,t,α] via a convex optimization algorithm. The output of the sensor extrinsic calibrations may be a pair of rotation and translation matrices on a per sensor basis with respect to the origin of the 3D space (e.g., as identified via LIDAR).

After the calibration parameters are solved for, tests for the numerical sensitivity of the solution can be performed. This may include, for example, verifying the Jacobian of the solution is near zero in all directions and that the covariance of each parameter is reasonably small (e.g., below a threshold). More sophisticated routines that test for sensitivity to targets and constraints may also be performed.

In some examples, movement of the vehicle among the plurality of vehicle positions at operations 1210 and/or 1215 may be omitted. Instead, rotation of content displayed on one or more displays 275/788 around the vehicle 102 may simulate rotation of the vehicle 102 about a turntable 405. Similarly, different movements of content displayed on one or more displays 275/788 around the vehicle 102 may simulate different movements of the vehicle 102. Instead of different positions of the vehicle 102, the different sensor capture datasets are captured (at operations 1110 and 1115) when content (e.g., virtual camera targets 200H) that is displayed on one or more displays 275/788 is at different positions. The known positions in the calibration environment of operations 1120 and 1130 may be known display positions of the content as displayed on the one or more displays 275/788 relative to the one or more displays 275/788, relative to the calibration environment, relative to the vehicle, or some combination thereof.

FIG. 13 is a flow diagram illustrating operations for interactions between the vehicle and the turntable. In particular, FIG. 13 illustrates a process 1300.

At optional step 1305, the turntable 405, vehicle 102, or surveying system 610 identifies that the vehicle 102 is positioned on the platform of the motorized turntable. This may be identified using pressure sensors 735 of the turntable 405, a GNSS or triangulation-based positioning receiver of the vehicle 102 compared to a known location of the turntable 405, image/infrared/radar/lidar data captured by the surveying system 610 indicating that the vehicle 102 is positioned on motorized turntable 405, or some combination thereof In some cases, the pressure sensors 735 may be positioned under or beside the guide railing, for example close behind the “stop” wall or incline, to ensure that the vehicle will apply pressure to them. In other cases, the entire turntable is receptive as a pressure sensor. In any case, this information is communicated to the dynamic scene control bridge 710 and/or the computing device 745 of the turntable system 405, either within the turntable itself (if via sensors 735) or via the relevant transceiver(s) of FIG. 7 . In some cases, either sensor data capture by the sensors of the vehicle 102 or rotation of the platform 420 of the motorized turntable 405 may automatically begin once the pressure sensors identify that the vehicle 102 is on the platform 420 and/or once sensors identify that the vehicle 102 has stopped moving (e.g., IMU of the vehicle 102, regional pressure sensors of regions of the turntable platform 420 surface, scene surveying system 610 camera, or some combination thereof). Rotation of the platform 420 about the base 425 may occur first before sensor data capture if, for example, calibration is previously designated to start with the vehicle 102 rotated to a particular rotation orientation or rotation position that is not the same as the rotation orientation or rotation position that the vehicle 102 is in when it drives (or is otherwise placed) onto the platform 420.

In some cases, the rotation of the platform 420 of the turntable 405 about the base 425 via the motors 730 can manually be triggered instead of being based on, and automatically triggered by, detection of the vehicle at operation 1305, for example via an input received at the dynamic scene control bridge 710 and/or the computing device 745 of the turntable system 405 from a wired or wireless interface that itself receives an input from a human being, the wired or wireless interface being for example a keyboard or touchscreen or mouse or remote control communicating in a wired or wireless fashion with the dynamic scene control bridge 710 and/or the computing device 745 of the turntable system 405.

At operation 1310, one or more motors 730 of the motorized turntable 405 are activated to rotate the platform 420 of the motorized turntable 405 about the base 425 of the motorized turntable 405 (and therefore vehicle 102 on top of the platform 420 as well) from a first rotation orientation to a second rotation orientation in response to detection that the vehicle is on the turntable. The one or more motors 730 may be deactivated, causing the platform of the motorized turntable 405 (and therefore vehicle 102 on top of the platform 420 as well) to stop rotating about the base 425 with the platform 420 in the second orientation at the stop of rotation. The term “rotation orientation” may be used to refer to an angle, or angular orientation, or angular position. Other terms may be used in place of the term “rotation position,” such as “angle,” “angular position,” “angular orientation,” “position,” or “orientation.” The first rotation orientation and the second rotation orientation may be a predetermined angle away from each other, for example N degrees, where N is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or some amount in between any two of these numbers. The first rotation orientation and the second rotation orientation may be an angle away from each other that is determined by the internal computing system 110 of the vehicle 102, or by the dynamic scene control bridge 710, or by the computing device 745 of the turntable system 405, or by some combination thereof, based on which angle would likely be most efficient, comprehensive, or an optimal balance thereof, in completing calibration of the entire fields of view (FOV) of the sensors 180 of the vehicle 102.

At operation 1315, the vehicle 102 uses its IMU (or other rotation detection device) to check whether the vehicle 102 (and therefore the platform 420) is still rotating. As the IMU is a collection of accelerometers and/or gyroscopes and/or other motion or rotation detection devices, the vehicle 102 can alternately separately use accelerometers and/or gyroscopes and/or other motion or rotation detection devices that are among the vehicle 102's sensors 180 to determine this. Alternately, the turntable 405 may use one or more motion sensors of its own (e.g., accelerometer, gyroscope, IMU, or any other motion sensor discussed herein) to identify whether the platform 420 of the turntable 405 is still rotating about the base 425. Alternately still, the scene surveying system 610 may use one or more cameras to visually identify whether the platform of the turntable 405 and/or the vehicle 102 is still rotating. In some cases, the device that detects that the vehicle 102 and/or the platform 420 of the turntable 405 has stopped rotating relative to the base 425 (the vehicle computing system 110, the computing device 745 of the turntable 405, the scene surveying system 610, and/or the dynamic scene control bridge 710) can send a signal identifying the detected stop in rotation to any of the vehicle computing system 110, the computing device 745 of the turntable, the scene surveying system 610, or the dynamic scene control bridge 710.

If, at operation 1320, the vehicle 102 or turntable 405 or scene surveying system 610 determines that the rotation has stopped, step 1325 follows step 1320. Otherwise, step 1315 follows step 1320.

In addition, we may use the vehicle 102′s other sensors 180, such as one or more cameras, infrared sensors, radar sensors, lidar sensors, sonar sensors, and/or sodar sensors instead of or in addition to the IMU, accelerometers, gyroscopes, and/or motion/rotation detection devices to identify when the vehicle 102 (and thus the platform 420) is still rotating relative to the base 425 or not. With all of these sensors, rotation may be identified based on whether regions of the calibration environment that should be motionless—walls, the floor, the ceiling, targets that have not been configured and/or commanded to move, light sources 620, the scene surveying system 610—are changing location between sensor captures (indicating that the vehicle is rotating and/or in motion) or are stationary between sensor captures (indicating that the vehicle is stationary).

At operation 1325, the vehicle captures sensor data using one or more of its sensors while the vehicle 102 is at the second position. If, at operation 1330, the internal computing device 110 of the vehicle 102 determines that it has finished capturing sensor data while vehicle is at the second rotational orientation/position, then step 1335 follows step 1330, and optionally, the vehicle computing system 110 may send a sensor capture confirmation signal to a computing device associated with the turntable 405, such as dynamic scene control bridge 710 and/or the computing device 745 of the turntable system 405. The sensor capture confirmation signal may then be used as a signal that the turntable 405 is allowed to begin (and/or should begin) rotation of the platform 420 about the base 425 from the second rotation orientation to a next rotation orientation. Otherwise, if sensor data capture is not complete step 1325 follows step 1330.

If, at operation 1335, the internal computing device 110 of the vehicle 102 determines that sufficient data has been captured by the vehicle 102's sensors 180 to perform calibration—then no more rotations of the platform 420 and the vehicle 102 about the base 425 are needed and step 1340 follows step 1335, thus proceeding from sensor data capture to sensor calibration. Optionally, the vehicle computing system 110 may send a sensor capture completion signal to a computing device associated with the turntable 405, such as dynamic scene control bridge 710 and/or the computing device 745 of the turntable system 405. The sensor capture completion signal may then be used as a signal that the platform 420 of the turntable 405 is allowed to stop (and/or should stop) rotating about the base 425 altogether to allow the vehicle 102 to exit the turntable 405 and the calibration environment, or that the platform 425 of the turntable 405 is allowed to begin (and/or should begin) rotating about the base 425 to an exit orientation that allow the vehicle 102 to exit the turntable and the calibration environment (for example when the calibration environment includes many targets around the turntable 405 except for in an entrance/exit direction, as in FIG. 6A where an optimal entrance/exit direction is on the bottom-right due to lack of targets and obstacles generally in that direction or in FIGS. 6B-6D where the optimal entrance/exit direction is the gap in the display(s) 275 on the right). Otherwise, if the internal computing device 110 of the vehicle 102 does not determine that sufficient data has been captured by the vehicle 102's sensors 180 to perform calibration at operation 1335, then step 1310 follows after step 1335, to continue rotations of the platform 420 (and vehicle 102) about the base 425 of the motorized turntable system 405. Depending on the sensors 180 on the vehicle 102 and the data captured by the sensors 180, the sensors 180 may require one or more full 360 degree rotations of the vehicle 102 on the platform 420, or may require less than one full 360 degree rotation of the vehicle 102 on the platform 420. In one embodiment, sufficient data for calibration of a sensor may mean data corresponding to targets covering at least a subset of the complete field of view of a particular sensor (collectively over a number of captures), with the subset reaching and/or exceeding a threshold percentage (e.g., 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%, 100%).

Some sensors may require more data for calibration than others, and thus, one sensor may have captured sufficient data for calibration while another sensor might not. In such cases, step 1335 may refer to all sensors and thus go through the “NO” arrow if any of the sensors 180 hasn't captured sufficient data. Alternately, a particular sensor capturing sufficient data, or a majority of sensors capturing sufficient data, may be the deciding factor toward “YES” or “NO.” In some cases, step 1335 may refer to each sensor separately, and once a particular sensor has captured sufficient data at operation 1335, that sensor may continue on to step 1340 for calibration even if the vehicle 102 on the platform 420 continues to rotate about the base 425 and the remaining sensors continue to capture data. Thus, step 1335 may enable staggered completion of capture of sufficient data for different sensors at different times.

In some cases, sensor data capture and sensor calibration occurs at least partially in parallel; that is, a time period in which sensor data capture occurs may at least partially overlap with a time period in which sensor calibration occurs. In such cases, the sensor may calibrate region by region, for example by calibrating the sensor in one or more regions in which the sensor detects (e.g., “sees”) targets for each data capture until the entire point of view of the sensor, or some sufficient subset is calibrated, with the subset reaching and/or exceeding a threshold percentage (e.g., 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%, 100%). Calibration of each sensor may use a different threshold, or certain sensors may share a threshold. When calibration occurs in parallel with capture rather than calibration on the whole sequentially following capture on the whole, step 1335 can simply identify when calibration of one or more sensors has successfully completed, and treat that as a proxy for identifying whether sufficient data is captured by those sensors to perform calibration.

In this case, however, step 1310 now rotates the vehicle 102 from the second position to a third position, and steps 1325 and 1330 refer to the third position. The next time step 1310 is reached in this fashion, it now rotates the vehicle 102 from the third position to a fourth position, and so on. In this way, step 1310 rotates the vehicle 102 on the platform 420 about the base 425 from its current rotational orientation/position to its next rotational orientation/position.

At operation 1340, the internal computing device 110 of the vehicle 102 proceeds on from sensor data capture to actual calibration of the sensors, for example as in steps 1125-1145 of FIG. 11 or steps 1225-1250 of FIG. 12 . To clarify, as discussed further above, capturing data via the sensors 180 as in steps 1325-1335 and calibrating the sensors 180 as in step 1340 can be performed in parallel, between rotations of the platform 420 and vehicle 102 about the base 425, or in any order that causes the process to be efficient. That is, calibration of data captured by a given one of the sensors 180 can begin immediately after the given sensor captures any new data, and can continue while the vehicle 102 is rotating about the base 425 on the platform 420 of the turntable 405 and while the sensors 180 capture further data. Because calibration and capture may be staggered and/or parallelized so that some further capture occurs after some calibration has started, dashed arrows extend from step 1340 to steps 1310 and 1325.

In some examples, the turntable 405 of operations 1305 and 1310 may be omitted. Instead, rotation of content displayed on one or more displays 275/788 around the vehicle 102 may simulate rotation of the vehicle 102 about a turntable 405. Instead of different positions of the vehicle 102, the different sensor capture datasets are captured (at operations 1325, 1330, and 1335) when content (e.g., virtual camera targets 200H) that is displayed on one or more displays 275/788 is at different positions. Operation 1310 can begin rotation/movement of the content (e.g., virtual camera targets 200H) about the one or more displays 275/788. Detection of rotation, or of the rotation stopping, at steps 1315 and 1320 can be performed by the display control system 790 stopping movement of the rotation/movement of the content (e.g., virtual camera targets 200H) about the one or more displays 275/788 and in some cases notifying the vehicle 102 that the movement has stopped.

It should be understood that some of the steps of FIG. 13 (such as 1305, 1315, 1320, 1330, and 1335) may be performed in some cases, and may be omitted in others.

FIG. 14A is a flow diagram illustrating operations for interactions between the vehicle and a lighting system. In particular, FIG. 14A illustrates a process 1400.

At operation 1405, the vehicle 102 captures sensor datasets using its sensors 180, for example as in steps 1010, 1110, 1210, 1215, and/or 1325. At operation 1410, the internal computing system 110 of the vehicle 102 identifies whether a characteristic of one or more sensor targets—in this case lighting and/or heating and/or display conditions in at least one area of the calibration environment that includes one or more sensor targets—are suboptimal, at least for the purposes of calibration. This may include lighting of one or more displays 275/788. This may include lighting from illuminated sensor targets that are backlit or frontlit as well as sensor targets that are illuminated via exterior light sources 620, such as spotlights. This may include heat sources 625 such as heat lamps. In some cases, the computer 110 of the vehicle 102 may identify that a representation of a sensor target that is identified within a sensor dataset (such as a photo or video) captured using the sensor (such as a camera) is suboptimal or not suitable for calibration, for example because the sensor target is too dimly lit, too brightly lit, lit using an incorrect color, or lit from the wrong angle (e.g., causing glare, shadows, dimness, brightness, uneven lighting, or otherwise affecting the representation of the sensor target). Lighting conditions may be suboptimal because they may cause a sensor to not properly or clearly detect out one or more features of the sensor target, such as a checkerboard pattern 210A, an ArUco pattern 210B, a crosshair pattern 210C, a shape 215 of a radar target 220, an aperture 225/240 and/or marking 230 and/or target ID 235 of a combined camera/depth sensor target 250, a pattern 210G of a polyhedral sensor target 255, a pattern and/or shape of a virtual camera calibration target 200H, and/or an object in a visual scene 280 depicting a simulated event 285. For example, in operation 1410, it may be determined that certain settings of the one or more displays 275/788 are not optimal and are affecting a representation of a virtual camera sensor target 200H, such as to brightness settings, contrast settings, color settings, and the like.

If, at operation 1410, the computer 110 of the vehicle 102 determines that the lighting and/or heating and/or display conditions are suboptimal, then step 1410 is followed by step 1415; otherwise, step 1410 is followed by step 1420, at which point the vehicle proceeds from capture to sensor calibration of its sensors, for example as in steps 1015-1025, 1125-1140, 1225-1250, and/or 1340.

Note that, as discussed with respect to FIG. 13 , capture of data by the sensors 180 of the vehicle 102 may occur in parallel with calibration of the sensors 180 of the vehicle 102. This may cause calibration and capture to be staggered and/or parallelized so that some further capture occurs after some calibration has started, represented by the dashed arrow from step 1420 to step 1405.

At operation 1415, the internal computing system 110 of the vehicle 102 sends an environment adjustment signal or message to an environment adjustment system (in this case the lighting/heating system 760) to activate one or more actuators 762 and thereby adjust lighting conditions in the at least one area of the calibration environment. The one or more actuators 762 may control one or more motors associated with the lighting/heating system 760, one or more switches associated with the lighting/heating system 760, and/or one or more dimmers associated with the lighting/heating system 760, and/or one or more actuators for settings changes at display controllers 795 of the display control system 790, and/or one or more actuators for settings changes at the one or more displays 788 of the display control system 790. Upon receiving the environment adjustment signal or message from the vehicle 102, the lighting/heating system 760 and/or display control system 790 can activate the one or more actuators 762, and can thereby effect a modification to the characteristic (i.e., the lighting and/or heating condition) of the one or more sensor targets, for example by increasing or decreasing certain settings for one or more of the displays 788, by deactivating one or more of the displays 788, by turning off one or more of the displays 788, by increasing or decreasing light and/or heat output of one or more light and/or heat sources 620/625, by moving one or more light and/or heat sources 620/625 translationally, by rotating one or more light and/or heat sources 620/625 (i.e., moving the one or more light and/or heat sources 620/625 rotationally), by activating (i.e., turning on) one or more light and/or heat sources 620/625, by deactivating (i.e., turning off) one or more light and/or heat sources 620/625, by changing a frequency of light and/or heat emitted by the one or more light and/or heat sources 620/625, by otherwise modifying the one or more light and/or heat sources 620/625, or some combination thereof. Note that an increase in light and/or heat output as discussed herein may refer to increasing light/heat output of one or more light and/or heat sources 620/625, activating one or more one or more light and/or heat sources 620/625, and/or moving one or more light and/or heat sources 620/625. Note that a decrease in light and/or heat output as discussed herein may refer to decreasing light and/or heat output one or more light and/or heat sources 620/625, deactivating one or more one or more light and/or heat sources 620/625, and/or moving one or more light and/or heat sources 620/625. In some examples, the display control system 790 can make adjustments to settings of the one or more displays 275/788 in operation 1415, such as to brightness settings, contrast settings, color settings, and the like. In an illustrative example, brightness settings of the one or more displays 275/788 may be too bright, in which case the display control system 790 can dim them in operation 1415. In another illustrative example, brightness settings of the one or more displays 275/788 may be too dim, in which case the display control system 790 can brighten them in operation 1415.

After step 1415, the process returns to 1405 to capture the sensor data with newly-adjusted (i.e., optimized) lighting and/or heating and/or displays. The newly-adjusted lighting and/or heating and/or displays are then checked at operation 1410 to see whether the adjustment from step 1415 corrected the lighting and/or heating and/or display condition issue identified previously at operation 1410 (leading to step 1420), or if further adjustments are required (leading to step 1415 once again).

FIG. 14B is a flow diagram illustrating operations for interactions between the vehicle and a target control system. In particular, FIG. 14B illustrates a process 1450.

At operation 1425, the vehicle 102 captures sensor datasets using its sensors 180, for example as in steps 1010, 1110, 1210, 1215, 1325, and/or 1405. At operation 1430, the internal computing system 110 of the vehicle 102 identifies whether a characteristic of one or more sensor targets—in this case sensor target positioning of at least one target in the calibration environment is suboptimal, at least for the purposes of calibration. In some cases, the computer 110 of the vehicle 102 may identify that a representation of a sensor target that is identified within a sensor dataset (such as a photo or video or radar image/video or lidar image/video) captured using the sensor (such as a camera or radar or lidar sensor) is suboptimal or not suitable for calibration, for example because the sensor target is located in a position and/or facing an orientation in which the sensor cannot properly or clearly detect out one or more features of the sensor target, such as a checkerboard pattern 210A or ArUco pattern 210B or crosshair pattern 210C of a camera target 200, or a shape 215 of a radar target 220, or an aperture 225/240 and/or marking 230 and/or target ID 235 of a combined camera/depth sensor target 250, a pattern 210G of a polyhedral sensor target 255, a virtual camera calibration sensor 200H, or a portion of a visual scene 280 of a simulated event 285.

If, at operation 1430, the computer 110 of the vehicle 102 determines that the sensor target positioning is sub-optimal, then step 1430 is followed by step 1435; otherwise, step 1430 is followed by step 1440, at which point the vehicle proceeds from capture to sensor calibration of its sensors, for example as in steps 1015-1025, 1125-1140, 1225-1250, 1340, and/or 1420.

Note that, as discussed with respect to FIG. 13 , capture of data by the sensors 180 of the vehicle 102 may occur in parallel with calibration of the sensors 180 of the vehicle 102. This may cause calibration and capture to be staggered and/or parallelized so that some further capture occurs after some calibration has started, represented by the dashed arrow from step 1440 to step 1425.

At operation 1435, the internal computing system 110 of the vehicle 102 sends an environment adjustment signal or message to an environment adjustment system (in this case the target control system 770 and/or the display control system 790) to move the at least one physical or virtual sensor target to a more optimal position in the calibration environment. In the case of a virtual sensor target such as a virtual camera calibration sensor 200H, moving the virtual sensor target at operation 1435 may be performed by the display control system 790. The display control system 790 can change the pose of the virtual sensor target along the one or more displays 275/788 by changing the position of the virtual sensor target along the one or more displays 275/788 and/or by changing the orientation of the virtual sensor target along the one or more displays 275/788. For physical sensor targets, moving the sensor target at operation 1435 can be achieved by physically moving the sensor target and/or the stand 410. For example mobile robots may be used to move the sensor target and/or the stand 410 about the vehicle analysis environment. In some examples, mobile robots may be part of the stand 410, or may hold the stand. In some examples, motors and/or actuators 774/792 of the target control system 770 may be actuated to change the pose (e.g., position and/or orientation) of the sensor target(s). The one or more actuators 774/792 may control one or more motors and/or switches associated with the target control system 770 and/or the robotic scene control system 790. Upon receiving the environment adjustment signal or message from the vehicle 102, the target control system 770 and/or of the robotic scene control system 790 can activate the one or more actuators 774/792, and can thereby effect a modification to the characteristic (i.e., the location around the turntable, rotation about one or more axes, height above the surface that the turntable rests on) of the one or more sensor targets, for example by activating one or more mobile robots to drive and therefore that translationally move one or more targets and/or by activating one or more mobile robots to raise or lower a target higher or lower and/or by activating one or more motors of a mobile robot or target control system 770 that rotate one or more targets about one or more axes.

After step 1435, the process returns to 1425 to capture the sensor data with newly-moved (i.e., optimized) sensor target positioning. The newly-moved target positioning is then checked at operation 1430 to see whether the adjustment from step 1435 corrected the target positioning issue identified previously at operation 1430 (leading to step 1440), or if further adjustments are required (leading to step 1435 once again).

In some cases, the adjustment(s) to lighting of FIG. 14A and the adjustment(s) to target positioning of FIG. 14B may both occur following capture of the same sensor dataset with the same sensor, In such cases, the checks of steps 1410 and 1430 may be performed repeatedly, once after each adjustment in either target positioning or lighting, since movement of a sensor target may correct or ameliorate issues with lighting, and on the other hand, adjustment of lighting may also correct or ameliorate issues with target positioning. In such cases, the sending of messages, and the resulting adjustments, of steps 1415 and step 1435, can either be performed sequentially (and then tested at operations 1410 and/or 1430), or can be performed in parallel (and then tested at operations 1410 and/or 1430).

While various flow diagrams provided and described above, such as those in FIGS. 8, 9A, 9B, 10, 11, 12, 13, 14A, and 14B, may show a particular order of operations performed by some embodiments of the subject technology, it should be understood that such order is exemplary. Alternative embodiments may perform the operations in a different order, combine certain operations, overlap certain operations, or some combination thereof. It should be understood that unless disclosed otherwise, any process illustrated in any flow diagram herein or otherwise illustrated or described herein may be performed by a machine, mechanism, and/or computing system 1500 discussed herein, and may be performed automatically (e.g., in response to one or more triggers/conditions described herein), autonomously, semi-autonomously (e.g., based on received instructions), or a combination thereof. Furthermore, any action described herein as occurring in response to one or more particular triggers/conditions should be understood to optionally occur automatically response to the one or more particular triggers/conditions.

As described herein, one aspect of the present technology is the gathering and use of data available from various sources to improve quality and experience. The present disclosure contemplates that in some instances, this gathered data may include personal information. The present disclosure contemplates that the entities involved with such personal information respect and value privacy policies and practices, for example by encrypting such information.

FIG. 15 shows an example of computing system 1500, which can be for example any computing device making up internal computing system 110, remote computing system 150, (potential) passenger device executing rideshare app 170, or any component thereof in which the components of the system are in communication with each other using connection 1505. Connection 1505 can be a physical connection via a bus, or a direct connection into processor 1510, such as in a chipset architecture. Connection 1505 can also be a virtual connection, networked connection, or logical connection.

In some embodiments, computing system 1500 is a distributed system in which the functions described in this disclosure can be distributed within a datacenter, multiple data centers, a peer network, etc. In some embodiments, one or more of the described system components represents many such components each performing some or all of the function for which the component is described. In some embodiments, the components can be physical or virtual devices.

Example system 1500 includes at least one processing unit (CPU or processor) 1510 and connection 1505 that couples various system components including system memory 1515, such as read-only memory (ROM) 1520 and random access memory (RAM) 1525 to processor 1510. Computing system 1500 can include a cache of high-speed memory 1512 connected directly with, in close proximity to, or integrated as part of processor 1510.

Processor 1510 can include any general purpose processor and a hardware service or software service, such as services 1532, 1534, and 1536 stored in storage device 1530, configured to control processor 1510 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor 1510 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

To enable user interaction, computing system 1500 includes an input device 1545, which can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. Computing system 1500 can also include output device 1535, which can be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input/output to communicate with computing system 1500. Computing system 1500 can include communications interface 1540, which can generally govern and manage the user input and system output. The communication interface may perform or facilitate receipt and/or transmission wired or wireless communications via wired and/or wireless transceivers, including those making use of an audio jack/plug, a microphone jack/plug, a universal serial bus (USB) port/plug, an Apple® Lightning® port/plug, an Ethernet port/plug, a fiber optic port/plug, a proprietary wired port/plug, a BLUETOOTH® wireless signal transfer, a BLUETOOTH® low energy (BLE) wireless signal transfer, an IBEACON® wireless signal transfer, a radio-frequency identification (RFID) wireless signal transfer, near-field communications (NFC) wireless signal transfer, dedicated short range communication (DSRC) wireless signal transfer, 802.11 Wi-Fi wireless signal transfer, wireless local area network (WLAN) signal transfer, Visible Light Communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), Infrared (IR) communication wireless signal transfer, Public Switched Telephone Network (PSTN) signal transfer, Integrated Services Digital Network (ISDN) signal transfer, 3G/4G/5G/LTE cellular data network wireless signal transfer, ad-hoc network signal transfer, radio wave signal transfer, microwave signal transfer, infrared signal transfer, visible light signal transfer, ultraviolet light signal transfer, wireless signal transfer along the electromagnetic spectrum, or some combination thereof. The communications interface 1540 may also include one or more Global Navigation Satellite System (GNSS) receivers or transceivers that are used to determine a location of the computing system 1500 based on receipt of one or more signals from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the US-based Global Positioning System (GPS), the Russia-based Global Navigation Satellite System (GLONASS), the China-based BeiDou Navigation Satellite System (BDS), and the Europe-based Galileo GNSS. There is no restriction on operating on any particular hardware arrangement, and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

Storage device 1530 can be a non-volatile and/or non-transitory and/or computer-readable memory device and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, a floppy disk, a flexible disk, a hard disk, magnetic tape, a magnetic strip/stripe, any other magnetic storage medium, flash memory, memristor memory, any other solid-state memory, a compact disc read only memory (CD-ROM) optical disc, a rewritable compact disc (CD) optical disc, digital video disk (DVD) optical disc, a blu-ray disc (BDD) optical disc, a holographic optical disk, another optical medium, a secure digital (SD) card, a micro secure digital (microSD) card, a Memory Stick® card, a smartcard chip, a EMV chip, a subscriber identity module (SIM) card, a mini/micro/nano/pico SIM card, another integrated circuit (IC) chip/card, random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash EPROM (FLASHEPROM), cache memory (L1/L2/L3/L4/L5/L#), resistive random-access memory (RRAM/ReRAM), phase change memory (PCM), spin transfer torque RAM (STT-RAM), another memory chip or cartridge, and/or a combination thereof.

The storage device 1530 can include software services, servers, services, etc., that when the code that defines such software is executed by the processor 1510, it causes the system to perform a function. In some embodiments, a hardware service that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor 1510, connection 1505, output device 1535, etc., to carry out the function.

For clarity of explanation, in some instances, the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.

Any of the steps, operations, functions, or processes described herein may be performed or implemented by a combination of hardware and software services or services, alone or in combination with other devices. In some embodiments, a service can be software that resides in memory of a client device and/or one or more servers of a content management system and perform one or more functions when a processor executes the software associated with the service. In some embodiments, a service is a program or a collection of programs that carry out a specific function. In some embodiments, a service can be considered a server. The memory can be a non-transitory computer-readable medium.

In some embodiments, the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer-readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The executable computer instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, solid-state memory devices, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include servers, laptops, smartphones, small form factor personal computers, personal digital assistants, and so on. The functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures.

Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of implementations. Further and although some subject matter may have been described in language specific to examples of structural features and/or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims.

As described herein, one aspect of the present technology is the gathering and use of data available from various sources to improve quality and experience. The present disclosure contemplates that in some instances, this gathered data may include personal information. The present disclosure contemplates that the entities involved with such personal information respect and value privacy policies and practices. 

What is claimed is:
 1. A system for simulation, the system comprising: a motorized turntable that rotates a housing into a plurality of orientations over a course of a simulation time period; a camera coupled to the housing, wherein the camera captures a plurality of camera datasets observing one or more displays external to an autonomous vehicle (AV) over the course of the simulation time period; one or more memory units storing instructions; and one or more processors that execute the instructions, wherein execution of the instructions by the one or more processors causes the one or more processors to: identify an event occurring within a virtual environment over the course of the simulation time period as the motorized turntable rotates; and identify one or more actions to be taken by the AV in response to the event occurring within the virtual environment.
 2. The system of claim 1, wherein the housing is an autonomous vehicle.
 3. The system of claim 1, wherein the camera captures the plurality of camera datasets over the course of the simulation time period by capturing at least one of the plurality of camera datasets while the housing is at each of the plurality of orientations.
 4. The system of claim 1, wherein one or more portions of the virtual environment as displayed on the one or more displays move along a plurality of display positions on the one or more displays over the course of the simulation time period, wherein the camera captures the plurality of camera datasets over the course of the simulation time period by capturing at least one of the plurality of camera datasets while the one or more portions of the virtual environment are at each of the plurality of display positions on the one or more displays.
 5. The system of claim 1, wherein the one or more displays in an analysis environment include a display screen wall that displays at least one or more portions of the virtual environment.
 6. The system of claim 1, wherein one or more displays in an analysis environment include a projection surface, wherein a projector projects at least one or more portions of the virtual environment onto the projection surface.
 7. The system of claim 1, wherein the one or more displays include a curved display surface.
 8. The system of claim 1, wherein, to generate a mapped representation of the virtual environment, the one or more processors generate a three-dimensional (3D) representation of the virtual environment.
 9. The system of claim 1, wherein, to generate a mapped representation of the virtual environment, the one or more processors generate a two-dimensional (2D) representation of the virtual environment.
 10. The system of claim 1, wherein the event corresponds to an accident involving a collision with the housing.
 11. The system of claim 1, wherein the event corresponds to an object in the virtual environment coming within a threshold virtual distance of the housing based on a mapped representation of the virtual environment.
 12. The system of claim 1, wherein the one or more actions include at least one of driving straight, turning left, turning right, accelerating, decelerating, maintaining speed, changing one or more lanes, braking, swerving, turning on one or more lights, turning off the one or more lights, adjusting brightness of the one or more lights, opening one or more windows, closing the one or more windows, opening one or more doors, closing the one or more doors, unlocking the one or more doors, locking the one or more doors, adjusting a climate control, adjusting a radio, adjusting an audio system, turning on one or more wipers, turning off the one or more wipers, and honking a horn.
 13. The system of claim 1, wherein, to output the one or more actions, the one or more processors output signals targeted to other components of the housing that are configured to execute the one or more actions.
 14. A method for simulation, the method comprising: rotating a housing into a plurality of orientations over a course of a simulation time period; capturing a plurality of camera datasets observing one or more displays external to an autonomous vehicle (AV) over the course of the simulation time period; identifying an event occurring within a virtual environment over the course of the simulation time period as a motorized turntable rotates; and identifying one or more actions to be taken by the AV in response to the event occurring within the virtual environment.
 15. The method of claim 14, wherein the housing is rotated into a plurality of orientations by a motorized turntable over the course of the simulation time period, wherein the camera captures the plurality of camera datasets over the course of the simulation time period by capturing at least one of the plurality of camera datasets while the housing is at each of the plurality of orientations.
 16. The method of claim 14, wherein one or more portions of the virtual environment as displayed on the one or more displays move along a plurality of display positions on the one or more displays over the course of the simulation time period, wherein the camera captures the plurality of camera datasets over the course of the simulation time period by capturing at least one of the plurality of camera datasets while the one or more portions of the virtual environment are at each of the plurality of display positions on the one or more displays.
 17. A non-transitory computer readable storage medium having embodied thereon a program, wherein the program is executable by a processor to perform a method of simulation, the method comprising: rotating a housing into a plurality of orientations over a course of a simulation time period; capturing a plurality of camera datasets observing one or more displays external to an autonomous vehicle (AV) over the course of the simulation time period; identifying an event occurring within a virtual environment over the course of the simulation time period as a motorized turntable rotates; and identifying one or more actions to be taken by the AV in response to the event occurring within the virtual environment.
 18. The non-transitory computer readable storage medium of claim 17, wherein one or more portions of the virtual environment as displayed on the one or more displays move along a plurality of display positions on the one or more displays over the course of the simulation time period, wherein the camera captures the plurality of camera datasets over the course of the simulation time period by capturing at least one of the plurality of camera datasets while the one or more portions of the virtual environment are at each of the plurality of display positions on the one or more displays.
 19. The non-transitory computer readable storage medium of claim 17, wherein one or more displays in an analysis environment include a display screen wall that displays at least one or more portions of the virtual environment.
 20. The non-transitory computer readable storage medium of claim 17, wherein one or more displays in an analysis environment include a projection surface, wherein a projector projects at least one or more portions of the virtual environment onto the projection surface. 